> b) You shouldn't set max_depth=5. Instead, build fully developed trees
> (max_depth=None) or rather tune min_samples_split using
> cross-validation.
Dear Gilles,
I have set up a grid search:
"
tuned_parameters = [{'min_samples_split': [1,2,3,4,5,6,7,8,9]}]
scores = [('precision', precision_score),('recall', recall_score)]
for score_name, score_func in scores:
print "# Tuning hyper-parameters for %s" % score_name
clf_RF_gridsearched = GridSearchCV(RandomForestClassifier
(),tuned_parameters,score_func=score_func,n_jobs=1)
clf_RF_gridsearched = clf_RF_gridsearched.fit
(X_train,y_train,cv=5,n_jobs=1)
print clf_RF_gridsearched.best_estimator_
print "Grid scores on development set:"
for params, mean_score, scores in clf_RF_gridsearched.grid_scores_:
print "%0.3f (+/-%0.03f) for %r" % (mean_score, scores.std() / 2,
params)
y_true, y_pred = y_test, clf_RF_gridsearched.predict(X_test)
print "Detailed classification report:"
print classification_report(y_true, y_pred)
"
min_samples_split is apparently "7" (at least for the recall score).
"
0.335 (+/-0.072) for {'min_samples_split': 7}
"
Nonetheless, this score is still only decent.
BTW: How do I output the confusion matrix/overall_accuracy of the
development set?
Cheers,
Paul
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