code:multi_class
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code:multi_class [2015/10/05 15:13] asa created |
code:multi_class [2015/10/08 14:12] asa |
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| Let's use a One-vs-the-rest classifier on the [[https://archive.ics.uci.edu/ml/datasets/Iris | iris dataset]]. The data has four features that describe features of three types of iris flowers. | Let's use a One-vs-the-rest classifier on the [[https://archive.ics.uci.edu/ml/datasets/Iris | iris dataset]]. The data has four features that describe features of three types of iris flowers. | ||
| + | |||
| + | <code python> | ||
| + | In [1]: import numpy as np | ||
| + | |||
| + | In [2]: from sklearn import datasets | ||
| + | |||
| + | In [3]: from sklearn.multiclass import OneVsRestClassifier,OneVsOneClassifier | ||
| + | |||
| + | In [4]: from sklearn.svm import LinearSVC,SVC | ||
| + | |||
| + | In [5]: from sklearn import cross_validation | ||
| + | |||
| + | In [6]: iris = datasets.load_iris() | ||
| + | |||
| + | In [7]: X, y = iris.data, iris.target | ||
| + | |||
| + | In [8]: classifier = OneVsRestClassifier(LinearSVC()) | ||
| + | |||
| + | In [9]: print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) | ||
| + | 0.966666666667 | ||
| + | |||
| + | In [10]: classifier = OneVsOneClassifier(LinearSVC()) | ||
| + | |||
| + | In [11]: print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5))0.98 | ||
| + | |||
| + | In [12]: # does this mean that oneVsOne is better? not necessarily... | ||
| + | |||
| + | In [13]: classifier = OneVsRestClassifier(SVC(C=1, kernel='rbf', gamma=0.5)) | ||
| + | |||
| + | In [14]: print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5))0.98 | ||
| + | |||
| + | </code> | ||
| + | |||
| + | And here's the code without the python prompts to get in the way: | ||
| <file python multi_class.py> | <file python multi_class.py> | ||
| + | import numpy as np | ||
| from sklearn import datasets | from sklearn import datasets | ||
| - | from sklearn.multiclass import OneVsRestClassifier | + | from sklearn.multiclass import OneVsRestClassifier,OneVsOneClassifier |
| - | from sklearn.svm import LinearSVC | + | from sklearn.svm import LinearSVC,SVC |
| from sklearn import cross_validation | from sklearn import cross_validation | ||
| iris = datasets.load_iris() | iris = datasets.load_iris() | ||
| X, y = iris.data, iris.target | X, y = iris.data, iris.target | ||
| + | |||
| classifier = OneVsRestClassifier(LinearSVC()) | classifier = OneVsRestClassifier(LinearSVC()) | ||
| - | print cross_validation.cross_val_score(classifier, X, y, cv=5) | + | print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) |
| + | |||
| + | classifier = OneVsOneClassifier(LinearSVC()) | ||
| + | |||
| + | print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) | ||
| + | |||
| + | # does this mean that oneVsOne is better? not necessarily... | ||
| + | |||
| + | classifier = OneVsRestClassifier(SVC(C=1, kernel='rbf', gamma=0.5)) | ||
| + | |||
| + | print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) | ||
| </file> | </file> | ||
code/multi_class.txt · Last modified: 2016/10/11 12:57 by asa
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