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index.html

<!doctype html>
<html>
<head>
<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>

<link href='https://fonts.loli.net/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color:#ffffff; --text-color:#333333; --select-text-bg-color:#B5D6FC; --select-text-font-color:auto; --monospace:"Lucida Console",Consolas,"Courier",monospace; --title-bar-height:20px; }
.mac-os-11 { --title-bar-height:28px; }
html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; }
body { margin: 0px; padding: 0px; height: auto; bottom: 0px; top: 0px; left: 0px; right: 0px; font-size: 1rem; line-height: 1.42857; overflow-x: hidden; background: inherit; tab-size: 4; }
iframe { margin: auto; }
a.url { word-break: break-all; }
a:active, a:hover { outline: 0px; }
.in-text-selection, ::selection { text-shadow: none; background: var(--select-text-bg-color); color: var(--select-text-font-color); }
#write { margin: 0px auto; height: auto; width: inherit; word-break: normal; overflow-wrap: break-word; position: relative; white-space: normal; overflow-x: visible; padding-top: 36px; }
#write.first-line-indent p { text-indent: 2em; }
#write.first-line-indent li p, #write.first-line-indent p * { text-indent: 0px; }
#write.first-line-indent li { margin-left: 2em; }
.for-image #write { padding-left: 8px; padding-right: 8px; }
body.typora-export { padding-left: 30px; padding-right: 30px; }
.typora-export .footnote-line, .typora-export li, .typora-export p { white-space: pre-wrap; }
.typora-export .task-list-item input { pointer-events: none; }
@media screen and (max-width: 500px) {
  body.typora-export { padding-left: 0px; padding-right: 0px; }
  #write { padding-left: 20px; padding-right: 20px; }
  .CodeMirror-sizer { margin-left: 0px !important; }
  .CodeMirror-gutters { display: none !important; }
}
#write li > figure:last-child { margin-bottom: 0.5rem; }
#write ol, #write ul { position: relative; }
img { max-width: 100%; vertical-align: middle; image-orientation: from-image; }
button, input, select, textarea { color: inherit; font: inherit; }
input[type="checkbox"], input[type="radio"] { line-height: normal; padding: 0px; }
*, ::after, ::before { box-sizing: border-box; }
#write h1, #write h2, #write h3, #write h4, #write h5, #write h6, #write p, #write pre { width: inherit; }
#write h1, #write h2, #write h3, #write h4, #write h5, #write h6, #write p { position: relative; }
p { line-height: inherit; }
h1, h2, h3, h4, h5, h6 { break-after: avoid-page; break-inside: avoid; orphans: 4; }
p { orphans: 4; }
h1 { font-size: 2rem; }
h2 { font-size: 1.8rem; }
h3 { font-size: 1.6rem; }
h4 { font-size: 1.4rem; }
h5 { font-size: 1.2rem; }
h6 { font-size: 1rem; }
.md-math-block, .md-rawblock, h1, h2, h3, h4, h5, h6, p { margin-top: 1rem; margin-bottom: 1rem; }
.hidden { display: none; }
.md-blockmeta { color: rgb(204, 204, 204); font-weight: 700; font-style: italic; }
a { cursor: pointer; }
sup.md-footnote { padding: 2px 4px; background-color: rgba(238, 238, 238, 0.7); color: rgb(85, 85, 85); border-radius: 4px; cursor: pointer; }
sup.md-footnote a, sup.md-footnote a:hover { color: inherit; text-transform: inherit; text-decoration: inherit; }
#write input[type="checkbox"] { cursor: pointer; width: inherit; height: inherit; }
figure { overflow-x: auto; margin: 1.2em 0px; max-width: calc(100% + 16px); padding: 0px; }
figure > table { margin: 0px; }
tr { break-inside: avoid; break-after: auto; }
thead { display: table-header-group; }
table { border-collapse: collapse; border-spacing: 0px; width: 100%; overflow: auto; break-inside: auto; text-align: left; }
table.md-table td { min-width: 32px; }
.CodeMirror-gutters { border-right: 0px; background-color: inherit; }
.CodeMirror-linenumber { user-select: none; }
.CodeMirror { text-align: left; }
.CodeMirror-placeholder { opacity: 0.3; }
.CodeMirror pre { padding: 0px 4px; }
.CodeMirror-lines { padding: 0px; }
div.hr:focus { cursor: none; }
#write pre { white-space: pre-wrap; }
#write.fences-no-line-wrapping pre { white-space: pre; }
#write pre.ty-contain-cm { white-space: normal; }
.CodeMirror-gutters { margin-right: 4px; }
.md-fences { font-size: 0.9rem; display: block; break-inside: avoid; text-align: left; overflow: visible; white-space: pre; background: inherit; position: relative !important; }
.md-fences-adv-panel { width: 100%; margin-top: 10px; text-align: center; padding-top: 0px; padding-bottom: 8px; overflow-x: auto; }
#write .md-fences.mock-cm { white-space: pre-wrap; }
.md-fences.md-fences-with-lineno { padding-left: 0px; }
#write.fences-no-line-wrapping .md-fences.mock-cm { white-space: pre; overflow-x: auto; }
.md-fences.mock-cm.md-fences-with-lineno { padding-left: 8px; }
.CodeMirror-line, twitterwidget { break-inside: avoid; }
.footnotes { opacity: 0.8; font-size: 0.9rem; margin-top: 1em; margin-bottom: 1em; }
.footnotes + .footnotes { margin-top: 0px; }
.md-reset { margin: 0px; padding: 0px; border: 0px; outline: 0px; vertical-align: top; background: 0px 0px; text-decoration: none; text-shadow: none; float: none; position: static; width: auto; height: auto; white-space: nowrap; cursor: inherit; -webkit-tap-highlight-color: transparent; line-height: normal; font-weight: 400; text-align: left; box-sizing: content-box; direction: ltr; }
li div { padding-top: 0px; }
blockquote { margin: 1rem 0px; }
li .mathjax-block, li p { margin: 0.5rem 0px; }
li blockquote { margin: 1rem 0px; }
li { margin: 0px; position: relative; }
blockquote > :last-child { margin-bottom: 0px; }
blockquote > :first-child, li > :first-child { margin-top: 0px; }
.footnotes-area { color: rgb(136, 136, 136); margin-top: 0.714rem; padding-bottom: 0.143rem; white-space: normal; }
#write .footnote-line { white-space: pre-wrap; }
@media print {
  body, html { border: 1px solid transparent; height: 99%; break-after: avoid; break-before: avoid; font-variant-ligatures: no-common-ligatures; }
  #write { margin-top: 0px; padding-top: 0px; border-color: transparent !important; }
  .typora-export * { -webkit-print-color-adjust: exact; }
  .typora-export #write { break-after: avoid; }
  .typora-export #write::after { height: 0px; }
  .is-mac table { break-inside: avoid; }
  .typora-export-show-outline .typora-export-sidebar { display: none; }
}
.footnote-line { margin-top: 0.714em; font-size: 0.7em; }
a img, img a { cursor: pointer; }
pre.md-meta-block { font-size: 0.8rem; min-height: 0.8rem; white-space: pre-wrap; background: rgb(204, 204, 204); display: block; overflow-x: hidden; }
p > .md-image:only-child:not(.md-img-error) img, p > img:only-child { display: block; margin: auto; }
#write.first-line-indent p > .md-image:only-child:not(.md-img-error) img { left: -2em; position: relative; }
p > .md-image:only-child { display: inline-block; width: 100%; }
#write .MathJax_Display { margin: 0.8em 0px 0px; }
.md-math-block { width: 100%; }
.md-math-block:not(:empty)::after { display: none; }
.MathJax_ref { fill: currentcolor; }
[contenteditable="true"]:active, [contenteditable="true"]:focus, [contenteditable="false"]:active, [contenteditable="false"]:focus { outline: 0px; box-shadow: none; }
.md-task-list-item { position: relative; list-style-type: none; }
.task-list-item.md-task-list-item { padding-left: 0px; }
.md-task-list-item > input { position: absolute; top: 0px; left: 0px; margin-left: -1.2em; margin-top: calc(1em - 10px); border: none; }
.math { font-size: 1rem; }
.md-toc { min-height: 3.58rem; position: relative; font-size: 0.9rem; border-radius: 10px; }
.md-toc-content { position: relative; margin-left: 0px; }
.md-toc-content::after, .md-toc::after { display: none; }
.md-toc-item { display: block; color: rgb(65, 131, 196); }
.md-toc-item a { text-decoration: none; }
.md-toc-inner:hover { text-decoration: underline; }
.md-toc-inner { display: inline-block; cursor: pointer; }
.md-toc-h1 .md-toc-inner { margin-left: 0px; font-weight: 700; }
.md-toc-h2 .md-toc-inner { margin-left: 2em; }
.md-toc-h3 .md-toc-inner { margin-left: 4em; }
.md-toc-h4 .md-toc-inner { margin-left: 6em; }
.md-toc-h5 .md-toc-inner { margin-left: 8em; }
.md-toc-h6 .md-toc-inner { margin-left: 10em; }
@media screen and (max-width: 48em) {
  .md-toc-h3 .md-toc-inner { margin-left: 3.5em; }
  .md-toc-h4 .md-toc-inner { margin-left: 5em; }
  .md-toc-h5 .md-toc-inner { margin-left: 6.5em; }
  .md-toc-h6 .md-toc-inner { margin-left: 8em; }
}
a.md-toc-inner { font-size: inherit; font-style: inherit; font-weight: inherit; line-height: inherit; }
.footnote-line a:not(.reversefootnote) { color: inherit; }
.md-attr { display: none; }
.md-fn-count::after { content: "."; }
code, pre, samp, tt { font-family: var(--monospace); }
kbd { margin: 0px 0.1em; padding: 0.1em 0.6em; font-size: 0.8em; color: rgb(36, 39, 41); background: rgb(255, 255, 255); border: 1px solid rgb(173, 179, 185); border-radius: 3px; box-shadow: rgba(12, 13, 14, 0.2) 0px 1px 0px, rgb(255, 255, 255) 0px 0px 0px 2px inset; white-space: nowrap; vertical-align: middle; }
.md-comment { color: rgb(162, 127, 3); opacity: 0.8; font-family: var(--monospace); }
code { text-align: left; vertical-align: initial; }
a.md-print-anchor { white-space: pre !important; border-width: initial !important; border-style: none !important; border-color: initial !important; display: inline-block !important; position: absolute !important; width: 1px !important; right: 0px !important; outline: 0px !important; background: 0px 0px !important; text-decoration: initial !important; text-shadow: initial !important; }
.md-inline-math .MathJax_SVG .noError { display: none !important; }
.html-for-mac .inline-math-svg .MathJax_SVG { vertical-align: 0.2px; }
.md-fences-math .MathJax_SVG_Display, .md-math-block .MathJax_SVG_Display { text-align: center; margin: 0px; position: relative; text-indent: 0px; max-width: none; max-height: none; min-height: 0px; min-width: 100%; width: auto; overflow-y: visible; display: block !important; }
.MathJax_SVG_Display, .md-inline-math .MathJax_SVG_Display { width: auto; margin: inherit; display: inline-block !important; }
.MathJax_SVG .MJX-monospace { font-family: var(--monospace); }
.MathJax_SVG .MJX-sans-serif { font-family: sans-serif; }
.MathJax_SVG { display: inline; font-style: normal; font-weight: 400; line-height: normal; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; zoom: 90%; }
#math-inline-preview-content { zoom: 1.1; }
.MathJax_SVG * { transition: none 0s ease 0s; }
.MathJax_SVG_Display svg { vertical-align: middle !important; margin-bottom: 0px !important; margin-top: 0px !important; }
.os-windows.monocolor-emoji .md-emoji { font-family: "Segoe UI Symbol", sans-serif; }
.md-diagram-panel > svg { max-width: 100%; }
[lang="flow"] svg, [lang="mermaid"] svg { max-width: 100%; height: auto; }
[lang="mermaid"] .node text { font-size: 1rem; }
table tr th { border-bottom: 0px; }
video { max-width: 100%; display: block; margin: 0px auto; }
iframe { max-width: 100%; width: 100%; border: none; }
.highlight td, .highlight tr { border: 0px; }
mark { background: rgb(255, 255, 0); color: rgb(0, 0, 0); }
.md-html-inline .md-plain, .md-html-inline strong, mark .md-inline-math, mark strong { color: inherit; }
.md-expand mark .md-meta { opacity: 0.3 !important; }
mark .md-meta { color: rgb(0, 0, 0); }
@media print {
  .typora-export h1, .typora-export h2, .typora-export h3, .typora-export h4, .typora-export h5, .typora-export h6 { break-inside: avoid; }
}
.md-diagram-panel .messageText { stroke: none !important; }
.md-diagram-panel .start-state { fill: var(--node-fill); }
.md-diagram-panel .edgeLabel rect { opacity: 1 !important; }
.md-require-zoom-fix foreignobject { font-size: var(--mermaid-font-zoom); }
.md-fences.md-fences-math { font-size: 1em; }
.md-fences-math .MathJax_SVG_Display { margin-top: 8px; cursor: default; }
.md-fences-advanced:not(.md-focus) { padding: 0px; white-space: nowrap; border: 0px; }
.md-fences-advanced:not(.md-focus) { background: inherit; }
.typora-export-show-outline .typora-export-content { max-width: 1440px; margin: auto; display: flex; flex-direction: row; }
.typora-export-sidebar { width: 300px; font-size: 0.8rem; margin-top: 80px; margin-right: 18px; }
.typora-export-show-outline #write { --webkit-flex:2; flex: 2 1 0%; }
.typora-export-sidebar .outline-content { position: fixed; top: 0px; max-height: 100%; overflow: hidden auto; padding-bottom: 30px; padding-top: 60px; width: 300px; }
@media screen and (max-width: 1024px) {
  .typora-export-sidebar, .typora-export-sidebar .outline-content { width: 240px; }
}
@media screen and (max-width: 800px) {
  .typora-export-sidebar { display: none; }
}
.outline-content li, .outline-content ul { margin-left: 0px; margin-right: 0px; padding-left: 0px; padding-right: 0px; list-style: none; }
.outline-content ul { margin-top: 0px; margin-bottom: 0px; }
.outline-content strong { font-weight: 400; }
.outline-expander { width: 1rem; height: 1.42857rem; position: relative; display: table-cell; vertical-align: middle; cursor: pointer; padding-left: 4px; }
.outline-expander::before { content: ""; position: relative; font-family: Ionicons; display: inline-block; font-size: 8px; vertical-align: middle; }
.outline-item { padding-top: 3px; padding-bottom: 3px; cursor: pointer; }
.outline-expander:hover::before { content: ""; }
.outline-h1 > .outline-item { padding-left: 0px; }
.outline-h2 > .outline-item { padding-left: 1em; }
.outline-h3 > .outline-item { padding-left: 2em; }
.outline-h4 > .outline-item { padding-left: 3em; }
.outline-h5 > .outline-item { padding-left: 4em; }
.outline-h6 > .outline-item { padding-left: 5em; }
.outline-label { cursor: pointer; display: table-cell; vertical-align: middle; text-decoration: none; color: inherit; }
.outline-label:hover { text-decoration: underline; }
.outline-item:hover { border-color: rgb(245, 245, 245); background-color: var(--item-hover-bg-color); }
.outline-item:hover { margin-left: -28px; margin-right: -28px; border-left: 28px solid transparent; border-right: 28px solid transparent; }
.outline-item-single .outline-expander::before, .outline-item-single .outline-expander:hover::before { display: none; }
.outline-item-open > .outline-item > .outline-expander::before { content: ""; }
.outline-children { display: none; }
.info-panel-tab-wrapper { display: none; }
.outline-item-open > .outline-children { display: block; }
.typora-export .outline-item { padding-top: 1px; padding-bottom: 1px; }
.typora-export .outline-item:hover { margin-right: -8px; border-right: 8px solid transparent; }
.typora-export .outline-expander::before { content: "+"; font-family: inherit; top: -1px; }
.typora-export .outline-expander:hover::before, .typora-export .outline-item-open > .outline-item > .outline-expander::before { content: "−"; }
.typora-export-collapse-outline .outline-children { display: none; }
.typora-export-collapse-outline .outline-item-open > .outline-children, .typora-export-no-collapse-outline .outline-children { display: block; }
.typora-export-no-collapse-outline .outline-expander::before { content: "" !important; }
.typora-export-show-outline .outline-item-active > .outline-item .outline-label { font-weight: 700; }


.CodeMirror { height: auto; }
.CodeMirror.cm-s-inner { background: inherit; }
.CodeMirror-scroll { overflow: auto hidden; z-index: 3; }
.CodeMirror-gutter-filler, .CodeMirror-scrollbar-filler { background-color: rgb(255, 255, 255); }
.CodeMirror-gutters { border-right: 1px solid rgb(221, 221, 221); background: inherit; white-space: nowrap; }
.CodeMirror-linenumber { padding: 0px 3px 0px 5px; text-align: right; color: rgb(153, 153, 153); }
.cm-s-inner .cm-keyword { color: rgb(119, 0, 136); }
.cm-s-inner .cm-atom, .cm-s-inner.cm-atom { color: rgb(34, 17, 153); }
.cm-s-inner .cm-number { color: rgb(17, 102, 68); }
.cm-s-inner .cm-def { color: rgb(0, 0, 255); }
.cm-s-inner .cm-variable { color: rgb(0, 0, 0); }
.cm-s-inner .cm-variable-2 { color: rgb(0, 85, 170); }
.cm-s-inner .cm-variable-3 { color: rgb(0, 136, 85); }
.cm-s-inner .cm-string { color: rgb(170, 17, 17); }
.cm-s-inner .cm-property { color: rgb(0, 0, 0); }
.cm-s-inner .cm-operator { color: rgb(152, 26, 26); }
.cm-s-inner .cm-comment, .cm-s-inner.cm-comment { color: rgb(170, 85, 0); }
.cm-s-inner .cm-string-2 { color: rgb(255, 85, 0); }
.cm-s-inner .cm-meta { color: rgb(85, 85, 85); }
.cm-s-inner .cm-qualifier { color: rgb(85, 85, 85); }
.cm-s-inner .cm-builtin { color: rgb(51, 0, 170); }
.cm-s-inner .cm-bracket { color: rgb(153, 153, 119); }
.cm-s-inner .cm-tag { color: rgb(17, 119, 0); }
.cm-s-inner .cm-attribute { color: rgb(0, 0, 204); }
.cm-s-inner .cm-header, .cm-s-inner.cm-header { color: rgb(0, 0, 255); }
.cm-s-inner .cm-quote, .cm-s-inner.cm-quote { color: rgb(0, 153, 0); }
.cm-s-inner .cm-hr, .cm-s-inner.cm-hr { color: rgb(153, 153, 153); }
.cm-s-inner .cm-link, .cm-s-inner.cm-link { color: rgb(0, 0, 204); }
.cm-negative { color: rgb(221, 68, 68); }
.cm-positive { color: rgb(34, 153, 34); }
.cm-header, .cm-strong { font-weight: 700; }
.cm-del { text-decoration: line-through; }
.cm-em { font-style: italic; }
.cm-link { text-decoration: underline; }
.cm-error { color: red; }
.cm-invalidchar { color: red; }
.cm-constant { color: rgb(38, 139, 210); }
.cm-defined { color: rgb(181, 137, 0); }
div.CodeMirror span.CodeMirror-matchingbracket { color: rgb(0, 255, 0); }
div.CodeMirror span.CodeMirror-nonmatchingbracket { color: rgb(255, 34, 34); }
.cm-s-inner .CodeMirror-activeline-background { background: inherit; }
.CodeMirror { position: relative; overflow: hidden; }
.CodeMirror-scroll { height: 100%; outline: 0px; position: relative; box-sizing: content-box; background: inherit; }
.CodeMirror-sizer { position: relative; }
.CodeMirror-gutter-filler, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-vscrollbar { position: absolute; z-index: 6; display: none; }
.CodeMirror-vscrollbar { right: 0px; top: 0px; overflow: hidden; }
.CodeMirror-hscrollbar { bottom: 0px; left: 0px; overflow: hidden; }
.CodeMirror-scrollbar-filler { right: 0px; bottom: 0px; }
.CodeMirror-gutter-filler { left: 0px; bottom: 0px; }
.CodeMirror-gutters { position: absolute; left: 0px; top: 0px; padding-bottom: 30px; z-index: 3; }
.CodeMirror-gutter { white-space: normal; height: 100%; box-sizing: content-box; padding-bottom: 30px; margin-bottom: -32px; display: inline-block; }
.CodeMirror-gutter-wrapper { position: absolute; z-index: 4; background: 0px 0px !important; border: none !important; }
.CodeMirror-gutter-background { position: absolute; top: 0px; bottom: 0px; z-index: 4; }
.CodeMirror-gutter-elt { position: absolute; cursor: default; z-index: 4; }
.CodeMirror-lines { cursor: text; }
.CodeMirror pre { border-radius: 0px; border-width: 0px; background: 0px 0px; font-family: inherit; font-size: inherit; margin: 0px; white-space: pre; overflow-wrap: normal; color: inherit; z-index: 2; position: relative; overflow: visible; }
.CodeMirror-wrap pre { overflow-wrap: break-word; white-space: pre-wrap; word-break: normal; }
.CodeMirror-code pre { border-right: 30px solid transparent; width: fit-content; }
.CodeMirror-wrap .CodeMirror-code pre { border-right: none; width: auto; }
.CodeMirror-linebackground { position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; z-index: 0; }
.CodeMirror-linewidget { position: relative; z-index: 2; overflow: auto; }
.CodeMirror-wrap .CodeMirror-scroll { overflow-x: hidden; }
.CodeMirror-measure { position: absolute; width: 100%; height: 0px; overflow: hidden; visibility: hidden; }
.CodeMirror-measure pre { position: static; }
.CodeMirror div.CodeMirror-cursor { position: absolute; visibility: hidden; border-right: none; width: 0px; }
.CodeMirror div.CodeMirror-cursor { visibility: hidden; }
.CodeMirror-focused div.CodeMirror-cursor { visibility: inherit; }
.cm-searching { background: rgba(255, 255, 0, 0.4); }
span.cm-underlined { text-decoration: underline; }
span.cm-strikethrough { text-decoration: line-through; }
.cm-tw-syntaxerror { color: rgb(255, 255, 255); background-color: rgb(153, 0, 0); }
.cm-tw-deleted { text-decoration: line-through; }
.cm-tw-header5 { font-weight: 700; }
.cm-tw-listitem:first-child { padding-left: 10px; }
.cm-tw-box { border-style: solid; border-right-width: 1px; border-bottom-width: 1px; border-left-width: 1px; border-color: inherit; border-top-width: 0px !important; }
.cm-tw-underline { text-decoration: underline; }
@media print {
  .CodeMirror div.CodeMirror-cursor { visibility: hidden; }
}


:root {
    --side-bar-bg-color: #fafafa;
    --control-text-color: #777;
}

@include-when-export url(https://fonts.loli.net/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext);

/* open-sans-regular - latin-ext_latin */
  /* open-sans-italic - latin-ext_latin */
    /* open-sans-700 - latin-ext_latin */
    /* open-sans-700italic - latin-ext_latin */
  html {
    font-size: 16px;
}

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</style><title>数据挖掘和机器学习在电池制造领域的应用</title>
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<body class='typora-export os-windows'><div class='typora-export-content'>
<div id='write'  class=''><center><font face="黑体" color="blue" size="5">数据挖掘和机器学习在锂离子电池工业领域的应用</font></center><p align="right"><font face="黑体" color="black" size="5">——吴睿滨</font></p><div class='md-toc' mdtype='toc'><p class="md-toc-content" role="list"><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n756"><a class="md-toc-inner" href="#导言">导言</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n757"><a class="md-toc-inner" href="#何为数据挖掘">何为数据挖掘？</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n758"><a class="md-toc-inner" href="#数据挖掘概述">数据挖掘概述</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n767"><a class="md-toc-inner" href="#数据挖掘过程">数据挖掘过程</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n773"><a class="md-toc-inner" href="#何为机器学习">何为机器学习？</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n774"><a class="md-toc-inner" href="#机器学习概述">机器学习概述</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n783"><a class="md-toc-inner" href="#机器学习过程">机器学习过程</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n791"><a class="md-toc-inner" href="#数据挖掘机器学习和人工智能">数据挖掘，机器学习和人工智能</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n801"><a class="md-toc-inner" href="#电池背后的数据应用逻辑">电池背后的数据应用逻辑</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n802"><a class="md-toc-inner" href="#数据指标可量化">数据指标可量化</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n807"><a class="md-toc-inner" href="#内联性强关系复杂">内联性强，关系复杂</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n814"><a class="md-toc-inner" href="#从数据到应用">从数据到应用</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n834"><a class="md-toc-inner" href="#锂离子电池制造一致性要求">锂离子电池制造一致性要求</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n838"><a class="md-toc-inner" href="#数据维度大人力">数据维度大，人力</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n839"><a class="md-toc-inner" href="#电池研制测试周期长时效性问题突出">电池研制测试周期长，时效性问题突出</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n840"><a class="md-toc-inner" href="#电池构成体系复杂变量组合巨大">电池构成体系复杂，变量组合巨大,</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n904"><a class="md-toc-inner" href="#文中出现的名词解释">文中出现的名词解释</a></span><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n841"><a class="md-toc-inner" href="#1项目安排">1.项目安排</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n1091"><a class="md-toc-inner" href="#11-项目目的">1.1 项目目的</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n842"><a class="md-toc-inner" href="#12-项目流程">1.2 项目流程</a></span><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n845"><a class="md-toc-inner" href="#2-数据收集清洗">2. 数据收集、清洗</a></span><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n846"><a class="md-toc-inner" href="#3-探索性数据分析数据预处理">3. 探索性数据分析/数据预处理</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n847"><a class="md-toc-inner" href="#31-概述">3.1 概述</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n849"><a class="md-toc-inner" href="#32-流程">3.2 流程</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n853"><a class="md-toc-inner" href="#33-查看数据信息">3.3 查看数据信息</a></span><span role="listitem" class="md-toc-item md-toc-h3" data-ref="n854"><a class="md-toc-inner" href="#331-数据理解">3.3.1 数据理解</a></span><span role="listitem" class="md-toc-item md-toc-h4" data-ref="n855"><a class="md-toc-inner" href="#3311-字段总数">3.3.1.1 字段总数</a></span><span role="listitem" class="md-toc-item md-toc-h4" data-ref="n859"><a class="md-toc-inner" href="#2312-初步特征选择">2.3.1.2 初步特征选择</a></span><span role="listitem" class="md-toc-item md-toc-h4" data-ref="n863"><a class="md-toc-inner" href="#2313-查看数据类型">2.3.1.3 查看数据类型</a></span><span role="listitem" class="md-toc-item md-toc-h1" data-ref="n882"><a class="md-toc-inner" href="#3-特征工程">3. 特征工程</a></span><span role="listitem" class="md-toc-item md-toc-h2" data-ref="n884"><a class="md-toc-inner" href="#31-特征选择">3.1 特征选择</a></span></p></div><h1 id='导言'><span>导言</span></h1><h2 id='何为数据挖掘'><span>何为数据挖掘？</span></h2><h3 id='数据挖掘概述'><span>数据挖掘概述</span></h3><ul><li><p><span>数据挖掘是一个用数据发现问题、解决问题的专业领域。</span></p></li><li><p><span>通常通过对数据的探索、处理、分析或建模实现。</span></p></li><li><p><span>把信息转化为价值，要经历信息、数据、知识、价值四个层面，数据挖掘就是中间的重要环节，是从数据中发现知识的过程。   </span></p><p><img src="D:\train\source\数据挖掘.jpg" alt="数据挖掘" style="zoom:67%;" /></p></li></ul><h3 id='数据挖掘过程'><span>数据挖掘过程</span></h3><ul><li><span>简单地可以分为：数据收集、数据预处理、数据探索分析、模型应用、评估、知识展示等。</span></li><li><span>具体地划分的话，每个过程又能分为多个分支，如数据预处理其实可以再细分为数据清洗、数据变换。如果再继续细分，数据清洗其实还可以再细分为异常值处理，缺失值处理，数据变换可以再细分为数据类型转换，数据量纲处理等。可以说数据挖掘作为一个学术领域，横跨多个学科，涵盖了统计学、数学、机器学习和计算机技术等，应用在比如金融、商业分析、工业等的各个行业领域。</span></li></ul><h2 id='何为机器学习'><span>何为机器学习？</span></h2><h3 id='机器学习概述'><span>机器学习概述</span></h3><ul><li><p><span>机器学习是一门多领域交叉学科，涉及概率论、统计学、线性代数、计算数学等多门学科，专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。机器学习是人工智能（Artificial Intelligence）的一个子集，同时它也是人工智能的核心，是使计算机具有智能的根本途径。（引用自：百度百科）</span></p></li><li><p><span>机器学习（machine learning, ML）根据不同的划分策略可以有不同的分类，比如以学习目的来划分，就可以简单分为：分类、回归。如果以学习方式来划分，可以分为：监督学习，半监督学习，无监督学习，强化学习。以应用层面来划分，机器学习可以应用在计算机视觉（Computer Vision, CV）、自然语言处理（Nature language processing, NLP）、数据挖掘（DataMining）等。当然如果以具体模型来划分，那就更为繁杂了。</span></p><p>&nbsp;</p></li></ul><p><img src="D:\train\source\机器学习.png" alt="'机器学习'" title="机器学习应用领域" style="zoom:67%;" /></p><p>&nbsp;</p><h3 id='机器学习过程'><span>机器学习过程</span></h3><ul><li><span>机器学习从高度抽象的角度看，其实可以分解为三大要素：模型（model)、策略（strategy）、算法（algorithm）。</span></li><li><span>从上面的要素出发，机器学习的过程实际上就是在实施这三个要素的过程：构建模型、确定策略、应用算法。但这种说法过于抽象，也损失不少细节。从应用层面上讲，其实机器学习是数据挖掘的一个工具或者某种意义上也可以理解为一个子集，它的实施过程也同数据挖掘一样：原材料获取（数据收集）—&gt; 材料处理加工（数据清洗、数据预处理、特征工程）—&gt; 材料性能分析（统计分析、建模分析）—&gt; 结果评价—&gt; 结果展示和应用。</span></li><li></li></ul><h2 id='数据挖掘机器学习和人工智能'><span>数据挖掘，机器学习和人工智能</span></h2><ul><li><span>数据挖掘是从现有的信息中提取数据的模式和模型，即精选出最重要的信息，以用于未来机器学习和AI的数据使用。数据挖掘本质上是机器学习和人工智能的基础，前者为后两者提供数据支撑。机器学习是人工智能很重要的一部分，前者是后者的子集，同时机器学习也是人工智能的一种实现（以面向对象的角度理解，前者也是后者的对象实例）。理论上人工智能几乎包括了所有机器能做的内容，当然也包括机器学习全部内容和部分数据挖掘的内容。</span></li><li><span>数据挖掘也会使用大量机器学习的算法，但是其特定的环境和目的和机器学习不太一样。</span></li><li><span>这三者的区别主要是目的不同，其手段(算法，模型)有很大的重叠，所以容易混淆。</span></li></ul><p><img src="D:\train\source\三者区别.jpg" alt="'数据挖掘，机器学习和人工智能之间的关系'" style="zoom:50%;" /></p><p>&nbsp;</p><h2 id='电池背后的数据应用逻辑'><span>电池背后的数据应用逻辑</span></h2><h3 id='数据指标可量化'><span>数据指标可量化</span></h3><ul><li><span>从1799年伏特发明电池至今，电池技术走过了200年的岁月，应用在电池设计、制造、测试等各个阶段的数据标准逐步完善，大部分性能和指标实现了量化。锂离子电池从被发明至今，也已经走过30多个年头，除了继承电池原有的数据标准外，又有了自己独特的发展。以工艺参数为例，得益于制造和检测装备的升级进步，我们能对各个工艺指标进行量化设计和监控。而成熟的数据标准和可量化意味着体系更容易提取数学模式，也更容易被数学模型解释，这也让数据挖掘和机器学习这个方面可以有所作为。</span></li></ul><h3 id='内联性强关系复杂'><span>内联性强，关系复杂</span></h3><ul><li><p><span>经验和各种物理、化学公式告诉我们，电池各个工艺构成，使用参数，环境参数等影响因素会对电池的最终性能指标会有着显著影响。比如，以电化学角度来理解：从电流—电势方程</span><sup><span>(1)</span></sup><span>(在离子扩散较快时，反应界面浓度与溶液本体浓度基本相等，方程演变为Bulter-Volmer方程）来看，凡是能影响界面面积A、反应速率k</span><sub><span>0</span></sub><span>、界面浓度C(0,t)、电极电势E、平衡电势E</span><sub><span>eq</span></sub><span>、体系温度T的因素都会对电池最终电性能产生影响。可问题不仅止于此，往下深究你会发现，反应界面面积跟电池结构设计，电解液分布，正负极活性物质颗粒特性状态等相关；标准反应速率又与参与反应的正负极材料特性、电解液特性（由阿伦尼乌斯方程</span><sup><span>(2)</span></sup><span>知道，不同材料体系的活化能Ea不同，前因子A也不同）等因素紧密相连；界面浓度被液相传质过程（主要是扩散过程），电解液特性，正负极颗粒特性这些过程因素控制；电极电势直接与材料体系，内阻分布，电池结构设计，荷电状态等因素相关......随着深入分析，你会发现，这完全是一个几近无限复杂的问题，影响最终结果的变量因素错综复杂，大部分的影响关系都无法用有限的数学方程确定，甚至很多都无法量化（比如材料特性就很难完全量化）。在高维特征空间内的数据分析，传统工具显得比较吃力，比如要了解多维特征空间的数据分布情况以及作解释性分析，传统的SAS软件（比如最常见的EXCEL，minitab, origin等，还有学习成本较高，面向数据专家的SAS，SPSS等）要不是力不从心，要不然就是对统计学知识要求太高。而高维空间的数据问题恰恰是机器学习的强项，机器学习不仅可以依靠模型得出较为准确的输出，也能一定程度为结果提供高维空间下的解释性分析（深度学习除外）。</span></p><div contenteditable="false" spellcheck="false" class="mathjax-block 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\large C_{Ox}:反应界面氧化态物质浓度\\
\large C_{Rd}:反应界面氧化态物质浓度\\
\large \alpha:传递系数，势垒对称系数\\
\large E:电极电势\\
\large E_{eq}:平衡电势\\
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\large T:绝对温度</script></div></div></li></ul><h3 id='从数据到应用'><span>从数据到应用</span></h3><ul><li><p><span>过往的电池设计是以历史经验为基础（其实就是技术人员的个人经验），固定大部分的特定变量组合（即不作为变量分析），针对特定问题进行有限变量（大部分是1-2维变量）分析，进而得出在这变量空间内的局部较优解，然后不断地优化，迭代上述步骤。这也是最普遍，最常见的做法。举个例子说明，我们从物理直觉上，可以判断出电池的使用寿命(cycle life)肯定与材料组成、工艺参数直接相关，这种相关性却无法被简单定量化表示，而要通过不断的变量组合、实验试错，但考虑到成本，效率，时效等原因，我们只能选取一到两个变量做对比实验，比如正极材料，电解液这两个变量组合，那么其它变量就被固化下来，如负极，隔膜等，等到有新的实验需求，然后在以同样的手段，更新迭代实验，进而得到符合我们预期的变量组合。这种做法本身是没有问题的，只是会有两个问题产生：</span></p><ol start='' ><li><p><span>实验数据没有被充分利用，假设目前有两组实验数据，a实验所采用的变量组合是</span><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="3.049ex" height="2.227ex" viewBox="0 -906.7 1312.8 958.9" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E6-MJMATHI-58" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 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52T339 48T401 46H427V0H416Q395 3 257 3Q121 3 100 0H88V46H114Q136 46 152 46T177 47T193 50T201 52T207 57T213 61V578Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E6-MJMATHI-58" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E6-MJMAIN-31" x="1215" y="513"></use></g></svg></span><script type="math/tex">X^1</script><span>，</span><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="3.049ex" height="2.227ex" viewBox="0 -906.7 1312.8 958.9" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E6-MJMATHI-58" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path><path stroke-width="0" id="E6-MJMAIN-31" d="M213 578L200 573Q186 568 160 563T102 556H83V602H102Q149 604 189 617T245 641T273 663Q275 666 285 666Q294 666 302 660V361L303 61Q310 54 315 52T339 48T401 46H427V0H416Q395 3 257 3Q121 3 100 0H88V46H114Q136 46 152 46T177 47T193 50T201 52T207 57T213 61V578Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E6-MJMATHI-58" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E6-MJMAIN-31" x="1215" y="513"></use></g></svg></span><script type="math/tex">X^1</script><span>是n维特征空间里的一个向量，</span><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="24.11ex" height="3.044ex" viewBox="0 -906.7 10380.8 1310.7" role="img" focusable="false" style="vertical-align: -0.938ex;"><defs><path stroke-width="0" id="E7-MJMATHI-58" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path><path stroke-width="0" id="E7-MJMAIN-31" d="M213 578L200 573Q186 568 160 563T102 556H83V602H102Q149 604 189 617T245 641T273 663Q275 666 285 666Q294 666 302 660V361L303 61Q310 54 315 52T339 48T401 46H427V0H416Q395 3 257 3Q121 3 100 0H88V46H114Q136 46 152 46T177 47T193 50T201 52T207 57T213 61V578Z"></path><path stroke-width="0" id="E7-MJMAIN-3D" d="M56 347Q56 360 70 367H707Q722 359 722 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y="-211"></use></g><use xlink:href="#E7-MJMAIN-5D" x="10102" y="0"></use></g></svg></span><script type="math/tex"> X^1=[x_1^1，x_2^1,x_3^1,...x^1_n] </script><span>,</span></p><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="3.049ex" height="2.227ex" viewBox="0 -906.7 1312.8 958.9" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E8-MJMATHI-58" d="M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 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x="10102" y="0"></use></g></svg></span><script type="math/tex"> X^2=[x_1^2，x_2^2,x_3^2,...x^2_n] </script><span>。如果两个实验想要对比得出直观的结论，起码要将变量控制在越少越好。通常需要将n维空间压缩到低维空间，这个过程</span></p></li><li><p><span>实验结果只能收敛于特定空间的局部较优解，很难获得全局较优解。</span></p></li></ol></li></ul><ul><li><p><span>比如举个例子，当前作锂离子电池设计时，要控制最终的电阻，其实很容易利用各个组成的数据指标来实现，比如电池的电阻就可以近似看成各个电池构成组件的电阻线性与各组件的接触电阻之和</span><sup><span>(2)</span></sup><span>，而各部件的电阻和接触电阻其实也可以由其内禀的电阻率和几何尺寸得出。换句话说，电池内阻的影响因素遍布整个变量空间，而非单一变量控制。当然这个例子是电池系统中相对简单的线性模型。</span></p><div contenteditable="false" spellcheck="false" class="mathjax-block md-end-block md-math-block md-rawblock" id="mathjax-n828" cid="n828" mdtype="math_block"><div class="md-rawblock-container md-math-container" tabindex="-1"><div class="MathJax_SVG_Display"><span class="MathJax_SVG" id="MathJax-Element-26-Frame" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="134.97ex" height="20.212ex" viewBox="0 -1836 58111.7 8702.4" role="img" 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k:the\; numbers\;of\;contacted \;components</script></div></div><p>&nbsp;</p><p><span>关系复杂，指的是，任一指标的最终</span></p><p><span>像这种有着明确物理模式的数据应用还有很多，但其它内联强但暂时没有明确的物理解释的数据组合其实从数学挖掘角度来看也能提供一定的分析视角。比如，我们从物理直觉上，可以判断出电池的使用寿命(cycle life)肯定与材料组成、工艺参数直接相关，但这种相关性却无法被简单定量化表示，而是通过不断的变量组合、实验试错，</span></p></li><li><p><span>相对于制造技术的发展，锂电池的数据挖掘工作其实还有很大的空间可以拓展。</span></p></li></ul><h3 id='锂离子电池制造一致性要求'><span>锂离子电池制造一致性要求</span></h3><ul><li><span>锂离子电池数据离散性指标往往意味着特征数据和标签数据</span></li></ul><h3 id='数据维度大人力'><span>数据维度大，人力</span></h3><h3 id='电池研制测试周期长时效性问题突出'><span>电池研制测试周期长，时效性问题突出</span></h3><h3 id='电池构成体系复杂变量组合巨大'><span>电池构成体系复杂，变量组合巨大,</span></h3><h2 id='文中出现的名词解释'><span>文中出现的名词解释</span></h2><ol><li><mark><span>特征</span></mark><span>：自变量，比如电解液种类，测试温度这些工艺变量，它是数据挖掘和机器学习的输入部分（Input);</span></li></ol><ol start='2' ><li><mark><span>标签</span></mark><span>：因变量，比如我们要预测的电池寿命，某一阶段的电池容量等，它是数据挖掘和机器学习的输出部分（Output);</span></li><li><mark><span>样本</span></mark><span>：数据集里拥有完整特征和标签的最小组成，即一个电芯的数据集合。</span></li><li></li></ol><h1 id='1项目安排'><span>1.项目安排</span></h1><h2 id='11-项目目的'><span>1.1 项目目的</span></h2><p>&nbsp;</p><h2 id='12-项目流程'><span>1.2 项目流程</span></h2><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><h1 id='2-数据收集清洗'><span>2. 数据收集、清洗</span></h1><h1 id='3-探索性数据分析数据预处理'><span>3. 探索性数据分析/数据预处理</span></h1><h2 id='31-概述'><span>3.1 概述</span></h2><p><span>EDA(Exploratory Data Analysis)-探索性数据分析是指对已有的数据（ 特别是调查或观察得来的原始数据 ）在尽量少的先验假定下进行探索通过作图、制表、方程拟合、计算特征量等手段探索数据的结构和规律的一种数据分析方法，是从数据中检测，纠正或删除损坏，不准确或不适用于模型的记录的过程 。作用是让人能够了解变量之间的相互关系以及变量与预测值之间的关系，然后进行相应的数据处理，让数据适应模型，匹配模型的需求，使得数据集进行机器学习或深度学习时，获得较好的效果。EDA过程其实就是机器学习里的数据预处理（Data Preprocessing）过程，只是叫法不同罢了。EDA过程可能面对的问题有：数据类型不同，比如有的是文字，有的是数字，有的含时间序列，有的连续，有的间断。也可能，数据的质量不行，有噪声，有异常，有缺失，数据出错，量纲不一，有重复，数据是偏态，数据量太大或太小 。本质上讲，EDA过程就是处理这些问题的过程。</span></p><h2 id='32-流程'><span>3.2 流程</span></h2><p><span>如果说数据收集、清洗是数据科学工作中最费时的工作，那么EDA过程应该就是数据挖掘或者机器学习中最细致的工作，它的成败好坏直接决定了数据挖掘和机器学习的结果。就如常言，算法决定了结果的下限，数据质量才决定了模型的上限。</span></p><p>&nbsp;</p><p><img src="d:/train/source/EDA.png" alt="EDA" title="EDA流程图" style="zoom:67%;" /></p><h2 id='33-查看数据信息'><span>3.3 查看数据信息</span></h2><h3 id='331-数据理解'><span>3.3.1 数据理解</span></h3><h4 id='3311-字段总数'><span>3.3.1.1 字段总数</span></h4><p><span>利用pandas.columns，pandas.shape命令直接查看数据情况，结果如下：</span></p><pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 9.5px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre>x</pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-comment"># 查看数据信息</span></span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">data</span>.<span class="cm-property">columns</span></span></pre><div class="" style="position: relative;"><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">Index</span>([<span class="cm-string">'times'</span>, <span class="cm-string">'capacity'</span>, <span class="cm-string">'循环次数表'</span>, <span class="cm-string">'all_capacity'</span>, <span class="cm-string">'上柜日期'</span>, <span class="cm-string">'设备号'</span>, <span class="cm-string">'行号'</span>, <span class="cm-string">'列号'</span>, <span class="cm-string">'日期条件列'</span>, <span class="cm-string">'批次条件列'</span>, <span class="cm-string">'试产单号/批次号'</span>, <span class="cm-string">'型号'</span>, <span class="cm-string">'容量mAh'</span>, <span class="cm-string">'工步'</span>, <span class="cm-string">'电解液'</span>, <span class="cm-string">'正极材料'</span>, <span class="cm-string">'负极材料'</span>, <span class="cm-string">'隔膜纸'</span>, <span class="cm-string">'2ex百分比'</span>, <span class="cm-string">'分容容量'</span>, <span class="cm-string">'首次容量'</span>, <span class="cm-string">'单次放电容量'</span>, <span class="cm-string">'异常升高次数'</span>, <span class="cm-string">'异常升高信息'</span>, <span class="cm-string">'异常断点次数'</span>, <span class="cm-string">'异常断点信息'</span>, <span class="cm-string">'温度'</span>, <span class="cm-string">'充电倍率'</span>, <span class="cm-string">'循环寿命'</span>, <span class="cm-string">'t500'</span>, <span class="cm-string">'t1000'</span>, <span class="cm-string">'t1500'</span>,<span class="cm-string">'t2000'</span>, <span class="cm-string">'t2500'</span>, <span class="cm-string">'t3000'</span>, <span class="cm-string">'t3500'</span>, <span class="cm-string">'t4000'</span>, <span class="cm-string">'t4500'</span>, <span class="cm-string">'t5000'</span>, <span class="cm-string">'t5500'</span>,<span class="cm-string">'t6000'</span>, <span class="cm-string">'t6500'</span>, <span class="cm-string">'t7000'</span>, <span class="cm-string">'t7500'</span>, <span class="cm-string">'t8000'</span>, <span class="cm-string">'t8500'</span>, <span class="cm-string">'t9000'</span>, <span class="cm-string">'t9500'</span>,<span class="cm-string">'t10000'</span>, <span class="cm-string">'t10500'</span>, <span class="cm-string">'t11000'</span>, <span class="cm-string">'t11500'</span>, <span class="cm-string">'t12000'</span>, <span class="cm-string">'t12500'</span>, <span class="cm-string">'t13000'</span>,<span class="cm-string">'t13500'</span>, <span class="cm-string">'t14000'</span>, <span class="cm-string">'t14500'</span>, <span class="cm-string">'t15000'</span>],<span class="cm-variable">dtype</span>=<span class="cm-string">'object'</span>)</span></pre></div><div class="" style="position: relative;"><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-comment"># 查看数据维度</span></span></pre></div><div class="" style="position: relative;"><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">data</span>.<span class="cm-property">shape</span></span></pre></div><div class="" style="position: relative;"><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">(<span class="cm-number">2659</span>, <span class="cm-number">59</span>)</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 230px;"></div><div class="CodeMirror-gutters" style="display: none; height: 230px;"></div></div></div></pre><p><span>在这里，对上述字段名称进行一些解释，方便大家理解特征的涵义。&#39;times&#39;、 &#39;capacity&#39;、 &#39;循环次数表&#39;、&#39;all_capacity&#39; 这四个特征，分别是在数据收集阶段，从原始数据收集的关于电池大循环（全充放循环）在周期中的分布数组、关于电池大循环容量（全充放循环，即电池经历若干次循环后的实际容量）在容量中的分布数组、电池在500次循环间隔取得的最为接近的大循环序号数组、电池在500次循环间隔取得的最为接近的大循环对应容量数组。可能这么说有点不好理解，不过没关系，这对接下来的操作和解释没有影响，因为这四列严格上来讲不是特征列，只是在程序设计阶段，方便操作而设计，最终也不会用在建模中，大家可以不用太关注。&#39;2ex百分比&#39;是指电池以2倍工作放电容量的工步占工作循环的百分比。t500是指循环次数最为接近500次循环的大循环容量（其实就是经历过500次循环后电池的实际容量，只不过电池测试设计时，并没有在500，1000等这样的间隔点设置全充放工步，这里是取最为接近500间隔的数值，方便分析），同理 t1000、t1500等也是如此。’工步‘是指从数据文件中提取的工步信息，’充电倍率‘是指从工艺登记（循环登记表）文件中所提取的工步信息，两者理论上是一致的。’循环寿命‘是指电池达到设定寿命时所经历的循环次数，对于不同型号的电池，设定寿命标准不同（比如LFP10370C2-200mAh就是以165mAh容量为限，亦或者触发测试程序的电压下限)。从数据维度上看，总共2659行（即2659个样本），59列（28个特征列，即特征维度为28维；31个标签列，即标签维度为31维，对于每个样本并没有一定有31个输出，比如有些电芯在8000次循环就到寿命或结束测试了，后面的循环数据就没有了）。</span></p><h4 id='2312-初步特征选择'><span>2.3.1.2 初步特征选择</span></h4><p><span>其实特征选择是属于后续</span><a href="#特征工程"><span>特征工程</span></a><span>的一部分，主要作用是寻找对模型有较高影响的特征，摒弃对模型没有影响，甚至是降低模型精度的特征，从而降低数据维度，减少花销，提高模型表现。之所以在EDA阶段就进行特征选择，其实主要是出于结果可视化的考虑，因为如果有太多特征列，就会导致查看统计信息时比较不直观，同时也是便于缺失值分析和异常值分析的降维考虑。特征选择后的结果如下：</span></p><pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang="python"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 9.25px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre>x</pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-variable">Index</span>([<span class="cm-string">'电解液'</span>, <span class="cm-string">'正极材料'</span>, <span class="cm-string">'负极材料'</span>, <span class="cm-string">'隔膜纸'</span>, <span class="cm-string">'2ex百分比'</span>, <span class="cm-string">'单次放电容量'</span>, <span class="cm-string">'充电倍率'</span>, <span class="cm-string">'温度'</span>, <span class="cm-string">'分容容量'</span>,<span class="cm-string">'首次容量'</span>, <span class="cm-string">'循环寿命'</span>, <span class="cm-string">'t500'</span>, <span class="cm-string">'t1000'</span>, <span class="cm-string">'t1500'</span>, <span class="cm-string">'t2000'</span>, <span class="cm-string">'t2500'</span>, <span class="cm-string">'t3000'</span>,<span class="cm-string">'t3500'</span>, <span class="cm-string">'t4000'</span>, <span class="cm-string">'t4500'</span>, <span class="cm-string">'t5000'</span>, <span class="cm-string">'t5500'</span>, <span class="cm-string">'t6000'</span>, <span class="cm-string">'t6500'</span>, <span class="cm-string">'t7000'</span>,<span class="cm-string">'t7500'</span>, <span class="cm-string">'t8000'</span>, <span class="cm-string">'t8500'</span>, <span class="cm-string">'t9000'</span>, <span class="cm-string">'t9500'</span>, <span class="cm-string">'t10000'</span>, <span class="cm-string">'t10500'</span>,<span class="cm-string">'t11000'</span>, <span class="cm-string">'t11500'</span>, <span class="cm-string">'t12000'</span>, <span class="cm-string">'t12500'</span>, <span class="cm-string">'t13000'</span>, <span class="cm-string">'t13500'</span>, <span class="cm-string">'t14000'</span>,<span class="cm-string">'t14500'</span>, <span class="cm-string">'t15000'</span>],<span class="cm-variable">dtype</span>=<span class="cm-string">'object'</span>)</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 92px;"></div><div class="CodeMirror-gutters" style="display: none; height: 92px;"></div></div></div></pre><p><span>之所以做这样的选择，具体原因会在</span><a href='#特征工程'><span>特征工程</span></a><span>的章节详细解释。这里简略说一下，留下的特征组合是分为两部分，一部分是电池的工艺特征部分[&#39;电解液&#39;, &#39;正极材料&#39;, &#39;负极材料&#39;, &#39;隔膜纸&#39;]，另一部分是电池的测试工艺特征部分[&#39;2ex百分比&#39;, &#39;单次放电容量&#39;, &#39;充电倍率&#39;, &#39;温度&#39;, &#39;分容容量&#39;, &#39;首次容量&#39;]。剩下的[&#39;循环寿命&#39;, &#39;t500&#39;, &#39;t1000&#39;……]是属于标签部分，是我们模型要预测的输出（output)，不是特征(input)。</span></p><h4 id='2313-查看数据类型'><span>2.3.1.3 查看数据类型</span></h4><p><span>不同的数据类型有着不同的处理方式，一般数据根据信息构成可以分为：</span></p><ul><li><span>定量数据：指的是一些数值，用于衡量某件东西的数量，根据数值的取值，大部分为连续变量数据。</span></li><li><span>定性数据：指的是一些类别，用于描述某件东西的性质，定性数据是离散变量数据。</span></li></ul><p><span>实际的数据类型会比较复杂，下面的分类有一定的借鉴意义：</span></p><figure><table><thead><tr><th style='text-align:left;' ><span>数据类型</span></th><th><span>数据名称</span></th><th><span>数学含义</span></th><th><span>描述</span></th><th><span>举例</span></th><th><span>可用操作</span></th></tr></thead><tbody><tr><td style='text-align:left;' ><span>离散，定性</span></td><td><span>名义</span></td><td><span>=，</span></td><td><span>名义变量就是不同的名字，用来告诉我们，这两个数据是否相同</span></td><td><span>邮编，性别，工号</span></td><td><span>众数，信息熵，相关性分析，卡方检验</span></td></tr><tr><td style='text-align:left;' ><span>离散，定性</span></td><td><span>有序</span></td><td><span>&lt;，&gt;</span></td><td><span>有序变量为数据的相对大小提供信息，告诉我们数据的顺序，但数据之间的大小间隔不具有固定意义，因此有序变量不能加减</span></td><td><span>学历，硬度</span></td><td><span>中位数，分位数，非参分析，符号检验</span></td></tr><tr><td style='text-align:left;' ><span>连续，定量</span></td><td><span>有距</span></td><td><span>+，-</span></td><td><span>有距变量之间的间隔是有固定意义的，可以加减，比如同一单位量纲</span></td><td><span>摄氏度温度</span></td><td><span>均值，标准差，皮尔逊相关分析，t检验，F检验</span></td></tr><tr><td style='text-align:left;' ><span>连续，定量</span></td><td><span>比率</span></td><td><span>*，/</span></td><td><span>比变量之间的间隔和比例本身都是有意义的，既可减又可乘除</span></td><td><span>电流，质量</span></td><td><span>几何平均，调和平均，百分数，变化量</span></td></tr></tbody></table></figure><p><span>用pandas.info()命令查看数据类型，结果如下：</span></p><pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang="python" style="break-inside: unset;"><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang="python"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 9.5px; left: 8px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation" style=""><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-comment"># &nbsp; Column &nbsp;  Non-Null Count  Dtype &nbsp;</span></span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-operator">---</span> &nbsp;<span class="cm-operator">------</span> &nbsp; &nbsp;<span class="cm-operator">--------------</span> &nbsp;<span class="cm-operator">-----</span> &nbsp;</span></pre><div class="" style="position: relative;"><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">0</span> &nbsp; <span class="cm-variable">电解液</span> &nbsp; &nbsp; &nbsp; <span class="cm-number">2656</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">1</span> &nbsp; <span class="cm-variable">正极材料</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2656</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">2</span> &nbsp; <span class="cm-variable">负极材料</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2656</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">3</span> &nbsp; <span class="cm-variable">隔膜纸</span> &nbsp; &nbsp; &nbsp; <span class="cm-number">2656</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">4</span> &nbsp; <span class="cm-number">2</span><span class="cm-variable">ex百分比</span> &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">float64</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">5</span> &nbsp; <span class="cm-variable">单次放电容量</span> &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">int64</span> &nbsp;</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">6</span> &nbsp; <span class="cm-variable">充电倍率</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2656</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">7</span> &nbsp; <span class="cm-variable">温度</span> &nbsp; &nbsp; &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-builtin">object</span> </span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">8</span> &nbsp; <span class="cm-variable">分容容量</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">float64</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">9</span> &nbsp; <span class="cm-variable">首次容量</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">float64</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number">10</span> &nbsp;<span class="cm-variable">循环寿命</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">1551</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">float64</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-number cm-error">11</span> &nbsp;<span class="cm-variable">t500</span> &nbsp; &nbsp; &nbsp;<span class="cm-number">2659</span> <span class="cm-variable">non</span><span class="cm-operator">-</span><span class="cm-variable">null</span> &nbsp; <span class="cm-variable">float64</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> <span class="cm-variable">……</span></span></pre></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 345px;"></div><div class="CodeMirror-gutters" style="display: none; height: 345px;"></div></div></div></pre><p><span>可以看出&#39;电解液&#39;，&#39;正极材料&#39;，&#39;负极材料&#39;，&#39;隔膜纸&#39;，’充电倍率‘，’温度‘  这几个特征是文本类别，是定性的离散变量，其余的特征和标签都为定量的连续变量。</span></p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><h1 id='3-特征工程'><span>3. 特征工程</span></h1><h2 id='31-特征选择'><span>3.1 特征选择</span></h2><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p></div></div>
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