Am 03.03.2016 um 13:43 schrieb Robert Pollak: > Hello list! > > I want to use parallel cross-validation and still get reproducible results. > In my code, I do > > if __name__ == '__main__': # This is necessary to use n_jobs > 1. > [...] > clf = DecisionTreeClassifier(max_depth=5) > cross_validation = StratifiedKFold(y, n_folds=10, shuffle=True, > random_state=0) > cross_val_prediction = cross_val_predict(clf, X, y, cv=cross_validation, > n_jobs=6) > > However, this gives different results than with n_jobs=1!
Oh, stupid me! I just forgot to set the random_state of the DecisionTreeClassifier. ------------------------------------------------------------------------------ Site24x7 APM Insight: Get Deep Visibility into Application Performance APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month Monitor end-to-end web transactions and take corrective actions now Troubleshoot faster and improve end-user experience. Signup Now! http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
