code:multi_class
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | |||
|
code:multi_class [2016/08/09 10:25] 127.0.0.1 external edit |
code:multi_class [2016/10/11 12:57] (current) asa |
||
|---|---|---|---|
| Line 3: | Line 3: | ||
| 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> | + | <file python multi_class.py> |
| - | 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> | ||
| import numpy as np | import numpy as np | ||
| Line 44: | Line 11: | ||
| from sklearn.svm import LinearSVC,SVC | from sklearn.svm import LinearSVC,SVC | ||
| from sklearn import cross_validation | from sklearn import cross_validation | ||
| + | |||
| + | # load the iris dataset: | ||
| + | |||
| iris = datasets.load_iris() | iris = datasets.load_iris() | ||
| X, y = iris.data, iris.target | X, y = iris.data, iris.target | ||
| + | # prepare cross validation folds | ||
| + | cv = cross_validation.StratifiedKFold(y, 5, shuffle=True, random_state=0) | ||
| + | |||
| + | # one-vs-the-rest | ||
| classifier = OneVsRestClassifier(LinearSVC()) | classifier = OneVsRestClassifier(LinearSVC()) | ||
| - | + | print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv))) | |
| - | print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) | + | # one-vs-one |
| classifier = OneVsOneClassifier(LinearSVC()) | classifier = OneVsOneClassifier(LinearSVC()) | ||
| + | print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv))) | ||
| - | print np.mean(cross_validation.cross_val_score(classifier, X, y, cv=5)) | + | # does this mean that one-vs-one is better? not necessarily... |
| - | + | ||
| - | # does this mean that oneVsOne is better? not necessarily... | + | |
| classifier = OneVsRestClassifier(SVC(C=1, kernel='rbf', gamma=0.5)) | classifier = OneVsRestClassifier(SVC(C=1, kernel='rbf', gamma=0.5)) | ||
| - | + | print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv))) | |
| - | 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
Page Tools
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International
