Learning scikit-learn: Machine Learning in Python by 2013

Author:2013
Language: eng
Format: mobi, epub
Publisher: Packt Publishing

Our tree has an accuracy of 0.838 on the training set. But remember that this is not a good indicator. This is especially true for decision trees as this method is highly susceptible to overfitting. Since we did not separate an evaluation set, we should apply cross-validation. For this example, we will use an extreme case of cross-validation, named leave-one-out cross-validation. For each instance in the training sample, we train on the rest of the sample, and evaluate the model built on the only instance left out. After performing as many classifications as training instances, we calculate the accuracy simply as the proportion of times our method correctly predicted the class of the left-out instance, and found it is a little lower (as we expected) than the resubstitution accuracy on the training set.

>>> from sklearn.cross_validation import cross_val_score, LeaveOneOut >>> from scipy.stats import sem >>> >>> def loo_cv(X_train, y_train,clf): >>> # Perform Leave-One-Out cross validation >>> # We are preforming 1313 classifications! >>> loo = LeaveOneOut(X_train[:].shape[0]) >>> scores = np.zeros(X_train[:].shape[0]) >>> for train_index, test_index in loo: >>> X_train_cv, X_test_cv = X_train[train_index], X_train[test_index] >>> y_train_cv, y_test_cv = y_train[train_index], y_train[test_index] >>> clf = clf.fit(X_train_cv,y_train_cv) >>> y_pred = clf.predict(X_test_cv) >>> scores[test_index] = metrics.accuracy_score( y_test_cv.astype(int), y_pred.astype(int)) >>> print ("Mean score: {0:.3f} (+/-{1:.3f})").format(np.mean(scores), sem(scores)) >>> loo_cv(X_train, y_train,clf) Mean score: 0.837 (+/-0.012)

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