-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathplot_gradient_boosting_oob.html
358 lines (291 loc) · 32.3 KB
/
plot_gradient_boosting_oob.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
<!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>Gradient Boosting Out-of-Bag estimates — scikit-learn 0.17.1 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: '0.17.1',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</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>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="top" title="scikit-learn 0.17.1 documentation" href="../../index.html" />
<link rel="up" title="Examples" href="../index.html" />
<link rel="next" title="Plot the decision surfaces of ensembles of trees on the iris dataset" href="plot_forest_iris.html" />
<link rel="prev" title="Feature transformations with ensembles of trees" href="plot_feature_transformation.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
</script>
</head>
<body role="document">
<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">
<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 0.17 (stable)</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="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<li><a href="http://scikit-learn.org/dev/documentation.html">Scikit-learn 0.18 (development)</a></li>
<li><a href="http://scikit-learn.org/0.16/documentation.html">Scikit-learn 0.16</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>
<div class="search_form">
<div 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">
<!-- rellinks[1:] is an ugly hack to avoid link to module
index -->
<div class="rellink">
<a href="plot_feature_transformation.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Feature trans...
</span>
<span class="hiddenrellink">
Feature transformations with ensembles of trees
</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">This documentation is for scikit-learn <strong>version 0.17.1</strong> — <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">If you use the software, please consider <a href="../../about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
<ul>
<li><a class="reference internal" href="#">Gradient Boosting Out-of-Bag estimates</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="section" id="gradient-boosting-out-of-bag-estimates">
<span id="example-ensemble-plot-gradient-boosting-oob-py"></span><h1>Gradient Boosting Out-of-Bag estimates<a class="headerlink" href="#gradient-boosting-out-of-bag-estimates" title="Permalink to this headline">¶</a></h1>
<p>Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the “optimal” number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be computed on-the-fly without the need for repeated model
fitting.
OOB estimates are only available for Stochastic Gradient Boosting
(i.e. <code class="docutils literal"><span class="pre">subsample</span> <span class="pre"><</span> <span class="pre">1.0</span></code>), the estimates are derived from the improvement
in loss based on the examples not included in the bootstrap sample
(the so-called out-of-bag examples).
The OOB estimator is a pessimistic estimator of the true
test loss, but remains a fairly good approximation for a small number of trees.</p>
<p>The figure shows the cumulative sum of the negative OOB improvements
as a function of the boosting iteration. As you can see, it tracks the test
loss for the first hundred iterations but then diverges in a
pessimistic way.
The figure also shows the performance of 3-fold cross validation which
usually gives a better estimate of the test loss
but is computationally more demanding.</p>
<img alt="../../_images/plot_gradient_boosting_oob_001.png" class="align-center" src="../../_images/plot_gradient_boosting_oob_001.png" />
<p><strong>Script output</strong>:</p>
<div class="highlight-python"><div class="highlight"><pre>Accuracy: 0.6800
</pre></div>
</div>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_gradient_boosting_oob.py"><code class="xref download docutils literal"><span class="pre">plot_gradient_boosting_oob.py</span></code></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="n">__doc__</span><span class="p">)</span>
<span class="c1"># Author: Peter Prettenhofer <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</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</span> <span class="kn">import</span> <span class="n">ensemble</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cross_validation.KFold.html#sklearn.cross_validation.KFold"><span class="n">KFold</span></a>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cross_validation.train_test_split.html#sklearn.cross_validation.train_test_split"><span class="n">train_test_split</span></a>
<span class="c1"># Generate data (adapted from G. Ridgeway's gbm example)</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="n">x3</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.exp.html#numpy.exp"><span class="n">np</span><span class="o">.</span><span class="n">exp</span></a><span class="p">(</span><span class="o">-</span><span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.sin.html#numpy.sin"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="n">x1</span><span class="p">)</span> <span class="o">-</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">x2</span> <span class="o">+</span> <span class="n">x3</span><span class="p">)))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.c_.html#numpy.c_"><span class="n">np</span><span class="o">.</span><span class="n">c_</span></a><span class="p">[</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">x3</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</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.cross_validation.train_test_split.html#sklearn.cross_validation.train_test_split"><span class="n">train_test_split</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="c1"># Fit classifier with out-of-bag estimates</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'n_estimators'</span><span class="p">:</span> <span class="mi">1200</span><span class="p">,</span> <span class="s1">'max_depth'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'subsample'</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span>
<span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span> <span class="s1">'min_samples_leaf'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'random_state'</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">ensemble</span><span class="o">.</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">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">acc</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Accuracy: {:.4f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">acc</span><span class="p">))</span>
<span class="n">n_estimators</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="s1">'n_estimators'</span><span class="p">]</span>
<span class="n">x</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.arange.html#numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">n_estimators</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
<span class="k">def</span> <span class="nf">heldout_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">):</span>
<span class="sd">"""compute deviance scores on ``X_test`` and ``y_test``. """</span>
<span class="n">score</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.zeros.html#numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">((</span><span class="n">n_estimators</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">y_pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">staged_decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)):</span>
<span class="n">score</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">loss_</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="k">return</span> <span class="n">score</span>
<span class="k">def</span> <span class="nf">cv_estimate</span><span class="p">(</span><span class="n">n_folds</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="n">cv</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.cross_validation.KFold.html#sklearn.cross_validation.KFold"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_folds</span><span class="o">=</span><span class="n">n_folds</span><span class="p">)</span>
<span class="n">cv_clf</span> <span class="o">=</span> <span class="n">ensemble</span><span class="o">.</span><span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">val_scores</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.zeros.html#numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">((</span><span class="n">n_estimators</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">cv</span><span class="p">:</span>
<span class="n">cv_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">train</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">train</span><span class="p">])</span>
<span class="n">val_scores</span> <span class="o">+=</span> <span class="n">heldout_score</span><span class="p">(</span><span class="n">cv_clf</span><span class="p">,</span> <span class="n">X_train</span><span class="p">[</span><span class="n">test</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">test</span><span class="p">])</span>
<span class="n">val_scores</span> <span class="o">/=</span> <span class="n">n_folds</span>
<span class="k">return</span> <span class="n">val_scores</span>
<span class="c1"># Estimate best n_estimator using cross-validation</span>
<span class="n">cv_score</span> <span class="o">=</span> <span class="n">cv_estimate</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="c1"># Compute best n_estimator for test data</span>
<span class="n">test_score</span> <span class="o">=</span> <span class="n">heldout_score</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="c1"># negative cumulative sum of oob improvements</span>
<span class="n">cumsum</span> <span class="o">=</span> <span class="o">-</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.cumsum.html#numpy.cumsum"><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span></a><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">oob_improvement_</span><span class="p">)</span>
<span class="c1"># min loss according to OOB</span>
<span class="n">oob_best_iter</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.argmin.html#numpy.argmin"><span class="n">np</span><span class="o">.</span><span class="n">argmin</span></a><span class="p">(</span><span class="n">cumsum</span><span class="p">)]</span>
<span class="c1"># min loss according to test (normalize such that first loss is 0)</span>
<span class="n">test_score</span> <span class="o">-=</span> <span class="n">test_score</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">test_best_iter</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.argmin.html#numpy.argmin"><span class="n">np</span><span class="o">.</span><span class="n">argmin</span></a><span class="p">(</span><span class="n">test_score</span><span class="p">)]</span>
<span class="c1"># min loss according to cv (normalize such that first loss is 0)</span>
<span class="n">cv_score</span> <span class="o">-=</span> <span class="n">cv_score</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">cv_best_iter</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.argmin.html#numpy.argmin"><span class="n">np</span><span class="o">.</span><span class="n">argmin</span></a><span class="p">(</span><span class="n">cv_score</span><span class="p">)]</span>
<span class="c1"># color brew for the three curves</span>
<span class="n">oob_color</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">256.0</span><span class="p">,</span> <span class="p">(</span><span class="mi">190</span><span class="p">,</span> <span class="mi">174</span><span class="p">,</span> <span class="mi">212</span><span class="p">)))</span>
<span class="n">test_color</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">256.0</span><span class="p">,</span> <span class="p">(</span><span class="mi">127</span><span class="p">,</span> <span class="mi">201</span><span class="p">,</span> <span class="mi">127</span><span class="p">)))</span>
<span class="n">cv_color</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">256.0</span><span class="p">,</span> <span class="p">(</span><span class="mi">253</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">134</span><span class="p">)))</span>
<span class="c1"># plot curves and vertical lines for best iterations</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">cumsum</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'OOB loss'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">oob_color</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">test_score</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Test loss'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">test_color</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">cv_score</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'CV loss'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">cv_color</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.axvline"><span class="n">plt</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">oob_best_iter</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">oob_color</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.axvline"><span class="n">plt</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">test_best_iter</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">test_color</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.axvline"><span class="n">plt</span><span class="o">.</span><span class="n">axvline</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">cv_best_iter</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">cv_color</span><span class="p">)</span>
<span class="c1"># add three vertical lines to xticks</span>
<span class="n">xticks</span> <span class="o">=</span> <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">()</span>
<span class="n">xticks_pos</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">xticks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="o">+</span>
<span class="p">[</span><span class="n">oob_best_iter</span><span class="p">,</span> <span class="n">cv_best_iter</span><span class="p">,</span> <span class="n">test_best_iter</span><span class="p">])</span>
<span class="n">xticks_label</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.array.html#numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="n">xticks</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> <span class="o">+</span>
<span class="p">[</span><span class="s1">'OOB'</span><span class="p">,</span> <span class="s1">'CV'</span><span class="p">,</span> <span class="s1">'Test'</span><span class="p">])</span>
<span class="n">ind</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.argsort.html#numpy.argsort"><span class="n">np</span><span class="o">.</span><span class="n">argsort</span></a><span class="p">(</span><span class="n">xticks_pos</span><span class="p">)</span>
<span class="n">xticks_pos</span> <span class="o">=</span> <span class="n">xticks_pos</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
<span class="n">xticks_label</span> <span class="o">=</span> <span class="n">xticks_label</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">(</span><span class="n">xticks_pos</span><span class="p">,</span> <span class="n">xticks_label</span><span class="p">)</span>
<a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">'normalized loss'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">'number of iterations'</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#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 example:</strong> 4.93 seconds
( 0 minutes 4.93 seconds)</p>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2010 - 2014, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/ensemble/plot_gradient_boosting_oob.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="plot_feature_transformation.html">Previous
</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 src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript"> google.load('search', '1',
{language : 'en'}); google.setOnLoadCallback(function() {
var customSearchControl = new
google.search.CustomSearchControl('016639176250731907682:tjtqbvtvij0');
customSearchControl.setResultSetSize(google.search.Search.FILTERED_CSE_RESULTSET);
var options = new google.search.DrawOptions();
options.setAutoComplete(true);
customSearchControl.draw('cse', options); }, true);
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>