forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_scaling_importance.html
409 lines (349 loc) · 31.6 KB
/
plot_scaling_importance.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Importance of Feature Scaling — scikit-learn 0.20.dev0 documentation</title>
<!-- htmltitle is before nature.css - we use this hack to load bootstrap first -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="stylesheet" href="../../_static/css/bootstrap.min.css" media="screen" />
<link rel="stylesheet" href="../../_static/css/bootstrap-responsive.css"/>
<link rel="stylesheet" href="../../_static/nature.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
VERSION: '0.20.dev0',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/js/copybutton.js"></script>
<script type="text/javascript" src="../../_static/js/extra.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_SVG"></script>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="search" title="Search" href="../../search.html" />
<link rel="next" title="Effect of transforming the targets in regression model" href="plot_transformed_target.html" />
<link rel="prev" title="Using PowerTransformer to apply the Box-Cox transformation" href="plot_power_transformer.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<script>
VERSION_SUBDIR = (function(groups) {
return groups ? groups[1] : null;
})(location.href.match(/^https?:\/\/scikit-learn.org\/([^\/]+)/));
</script>
<link rel="canonical" href="http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
function showMenu() {
var topNav = document.getElementById("scikit-navbar");
if (topNav.className === "navbar") {
topNav.className += " responsive";
} else {
topNav.className = "navbar";
}
};
</script>
</head>
<body>
<div class="header-wrapper">
<div class="header">
<p class="logo"><a href="../../index.html">
<img src="../../_static/scikit-learn-logo-small.png" alt="Logo"/>
</a>
</p><div class="navbar" id="scikit-navbar">
<ul>
<li><a href="../../index.html">Home</a></li>
<li><a href="../../install.html">Installation</a></li>
<li class="btn-li"><div class="btn-group">
<a href="../../documentation.html">Documentation</a>
<a class="btn dropdown-toggle" data-toggle="dropdown">
<span class="caret"></span>
</a>
<ul class="dropdown-menu">
<li class="link-title">Scikit-learn <script>document.write(DOCUMENTATION_OPTIONS.VERSION + (VERSION_SUBDIR ? " (" + VERSION_SUBDIR + ")" : ""));</script></li>
<li><a href="../../tutorial/index.html">Tutorials</a></li>
<li><a href="../../user_guide.html">User guide</a></li>
<li><a href="../../modules/classes.html">API</a></li>
<li><a href="../../glossary.html">Glossary</a></li>
<li><a href="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<script>if (VERSION_SUBDIR != "stable") document.write('<li><a href="http://scikit-learn.org/stable/documentation.html">Stable version</a></li>')</script>
<script>if (VERSION_SUBDIR != "dev") document.write('<li><a href="http://scikit-learn.org/dev/documentation.html">Development version</a></li>')</script>
<li><a href="http://scikit-learn.org/dev/versions.html">All available versions</a></li>
<li><a href="../../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
</ul>
</div>
</li>
<li><a href="../index.html">Examples</a></li>
</ul>
<a href="javascript:void(0);" onclick="showMenu()">
<div class="nav-icon">
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
</div>
</a>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
</div>
</div> <!-- end navbar --></div>
</div>
<!-- GitHub "fork me" ribbon -->
<a href="https://github.com/scikit-learn/scikit-learn">
<img class="fork-me"
style="position: absolute; top: 0; right: 0; border: 0;"
src="../../_static/img/forkme.png"
alt="Fork me on GitHub" />
</a>
<div class="content-wrapper">
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div class="rel">
<div class="rellink">
<a href="plot_power_transformer.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Using PowerTr...
</span>
<span class="hiddenrellink">
Using PowerTransformer to apply the Box-Cox transformation
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="plot_transformed_target.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
Effect of tra...
</span>
<span class="hiddenrellink">
Effect of transforming the targets in regression model
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../index.html">
Up
<br/>
<span class="smallrellink">
Examples
</span>
<span class="hiddenrellink">
Examples
</span>
</a>
</div>
</div>
<p class="doc-version"><b>scikit-learn v0.20.dev0</b><br/>
<a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">Please <b><a href="../../about.html#citing-scikit-learn" style="font-size: 110%;">cite us </a></b>if you use the software.</p>
<ul>
<li><a class="reference internal" href="#">Importance of Feature Scaling</a></li>
</ul>
</div>
</div>
<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
<label for="nav-trigger"></label>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">here</span></a> to download the full example code or run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="importance-of-feature-scaling">
<span id="sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"></span><h1>Importance of Feature Scaling<a class="headerlink" href="#importance-of-feature-scaling" title="Permalink to this headline">¶</a></h1>
<p>Feature scaling through standardization (or Z-score normalization)
can be an important preprocessing step for many machine learning
algorithms. Standardization involves rescaling the features such
that they have the properties of a standard normal distribution
with a mean of zero and a standard deviation of one.</p>
<p>While many algorithms (such as SVM, K-nearest neighbors, and logistic
regression) require features to be normalized, intuitively we can
think of Principle Component Analysis (PCA) as being a prime example
of when normalization is important. In PCA we are interested in the
components that maximize the variance. If one component (e.g. human
height) varies less than another (e.g. weight) because of their
respective scales (meters vs. kilos), PCA might determine that the
direction of maximal variance more closely corresponds with the
‘weight’ axis, if those features are not scaled. As a change in
height of one meter can be considered much more important than the
change in weight of one kilogram, this is clearly incorrect.</p>
<p>To illustrate this, PCA is performed comparing the use of data with
<a class="reference internal" href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal"><span class="pre">StandardScaler</span></code></a> applied,
to unscaled data. The results are visualized and a clear difference noted.
The 1st principal component in the unscaled set can be seen. It can be seen
that feature #13 dominates the direction, being a whole two orders of
magnitude above the other features. This is contrasted when observing
the principal component for the scaled version of the data. In the scaled
version, the orders of magnitude are roughly the same across all the features.</p>
<p>The dataset used is the Wine Dataset available at UCI. This dataset
has continuous features that are heterogeneous in scale due to differing
properties that they measure (i.e alcohol content, and malic acid).</p>
<p>The transformed data is then used to train a naive Bayes classifier, and a
clear difference in prediction accuracies is observed wherein the dataset
which is scaled before PCA vastly outperforms the unscaled version.</p>
<img alt="../../_images/sphx_glr_plot_scaling_importance_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_plot_scaling_importance_001.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none"><div class="highlight"><pre><span></span>Prediction accuracy for the normal test dataset with PCA
81.48%
Prediction accuracy for the standardized test dataset with PCA
98.15%
PC 1 without scaling:
[ 1.76e-03 -8.36e-04 1.55e-04 -5.31e-03 2.02e-02 1.02e-03 1.53e-03
-1.12e-04 6.31e-04 2.33e-03 1.54e-04 7.43e-04 1.00e+00]
PC 1 with scaling:
[ 0.13 -0.26 -0.01 -0.23 0.16 0.39 0.42 -0.28 0.33 -0.11 0.3 0.38
0.28]
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="View documentation for sklearn.preprocessing.StandardScaler"><span class="n">StandardScaler</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="View documentation for sklearn.naive_bayes.GaussianNB"><span class="n">GaussianNB</span></a>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="View documentation for sklearn.datasets.load_wine"><span class="n">load_wine</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="View documentation for sklearn.pipeline.make_pipeline"><span class="n">make_pipeline</span></a>
<span class="k">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Code source: Tyler Lanigan <[email protected]></span>
<span class="c1"># Sebastian Raschka <[email protected]></span>
<span class="c1"># License: BSD 3 clause</span>
<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">FIG_SIZE</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
<span class="n">features</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="View documentation for sklearn.datasets.load_wine"><span class="n">load_wine</span></a><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># Make a train/test split using 30% test size</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.30</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="c1"># Fit to data and predict using pipelined GNB and PCA.</span>
<span class="n">unscaled_clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="View documentation for sklearn.pipeline.make_pipeline"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="View documentation for sklearn.naive_bayes.GaussianNB"><span class="n">GaussianNB</span></a><span class="p">())</span>
<span class="n">unscaled_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">pred_test</span> <span class="o">=</span> <span class="n">unscaled_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Fit to data and predict using pipelined scaling, GNB and PCA.</span>
<span class="n">std_clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="View documentation for sklearn.pipeline.make_pipeline"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="View documentation for sklearn.preprocessing.StandardScaler"><span class="n">StandardScaler</span></a><span class="p">(),</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="View documentation for sklearn.naive_bayes.GaussianNB"><span class="n">GaussianNB</span></a><span class="p">())</span>
<span class="n">std_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">pred_test_std</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Show prediction accuracies in scaled and unscaled data.</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Prediction accuracy for the normal test dataset with PCA'</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'{:.2%}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="View documentation for sklearn.metrics.accuracy_score"><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test</span><span class="p">)))</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Prediction accuracy for the standardized test dataset with PCA'</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'{:.2%}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="View documentation for sklearn.metrics.accuracy_score"><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test_std</span><span class="p">)))</span>
<span class="c1"># Extract PCA from pipeline</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">unscaled_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">'pca'</span><span class="p">]</span>
<span class="n">pca_std</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">'pca'</span><span class="p">]</span>
<span class="c1"># Show first principal components</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">PC 1 without scaling:</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">PC 1 with scaling:</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">pca_std</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># Scale and use PCA on X_train data for visualization.</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">'standardscaler'</span><span class="p">]</span>
<span class="n">X_train_std</span> <span class="o">=</span> <span class="n">pca_std</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
<span class="c1"># visualize standardized vs. untouched dataset with PCA performed</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">FIG_SIZE</span><span class="p">)</span>
<span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'red'</span><span class="p">,</span> <span class="s1">'green'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'^'</span><span class="p">,</span> <span class="s1">'s'</span><span class="p">,</span> <span class="s1">'o'</span><span class="p">)):</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'class </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">l</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="n">m</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="s1">'blue'</span><span class="p">,</span> <span class="s1">'red'</span><span class="p">,</span> <span class="s1">'green'</span><span class="p">),</span> <span class="p">(</span><span class="s1">'^'</span><span class="p">,</span> <span class="s1">'s'</span><span class="p">,</span> <span class="s1">'o'</span><span class="p">)):</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train_std</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train_std</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'class </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">l</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="n">m</span>
<span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Training dataset after PCA'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Standardized training dataset after PCA'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'1st principal component'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'2nd principal component'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="View documentation for matplotlib.pyplot.tight_layout"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p><strong>Total running time of the script:</strong> ( 0 minutes 0.093 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-preprocessing-plot-scaling-importance-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn.github.io/master?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb"><img alt="https://static.mybinder.org/badge.svg" src="https://static.mybinder.org/badge.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_scaling_importance.py" download=""><code class="xref download docutils literal"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_scaling_importance.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_scaling_importance.ipynb" download=""><code class="xref download docutils literal"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_scaling_importance.ipynb</span></code></a></div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2018, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="plot_power_transformer.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="plot_transformed_target.html">Next
</a>
</div>
</div>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-22606712-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
<script>
(function() {
var cx = '016639176250731907682:tjtqbvtvij0';
var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s);
})();
</script>
</body>
</html>