Why not simply do something like this? >>> a array([[ 0., 5., 2.], [ 0., 0., 3.], [ 0., 0., 0.]]) >>> b array([ 0., 0., 3.]) >>> a==b array([[ True, False, False], [ True, True, True], [ True, True, False]], dtype=bool) >>> np.all(a==b,axis=1) array([False, True, False], dtype=bool) >>> np.where(np.all(a==b,axis=1)) (array([1]),)
A RandomForestClassifier is a collection of DecisionTreeClassifier‘s. No matter how big your training set, a decision tree simply returns: a decision. One class has probability 1, the other classes have probability 0. The RandomForest simply votes among the results. predict_proba() returns the number of votes for each class (each tree in the forest makes its … Read more
Each instance of RandomForestClassifier has an estimators_ attribute, which is a list of DecisionTreeClassifier instances. The documentation shows that an instance of DecisionTreeClassifier has a tree_ attribute, which is an instance of the (undocumented, I believe) Tree class. Some exploration in the interpreter shows that each Tree instance has a max_depth parameter which appears to … Read more
… import cPickle rf = RandomForestRegresor() rf.fit(X, y) with open(‘path/to/file’, ‘wb’) as f: cPickle.dump(rf, f) # in your prediction file with open(‘path/to/file’, ‘rb’) as f: rf = cPickle.load(f) preds = rf.predict(new_X)
I would really advise against using OOB to evaluate a model, but it is useful to know how to run a grid search outside of GridSearchCV() (I frequently do this so I can save the CV predictions from the best grid for easy model stacking). I think the easiest way is to create your grid … Read more
You can pass sample weights argument to Random Forest fit method sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, … Read more
Assuming your Random Forest model is already fitted, first you should first import the export_graphviz function: from sklearn.tree import export_graphviz In your for cycle you could do the following to generate the dot file export_graphviz(tree_in_forest, feature_names=X.columns, filled=True, rounded=True) The next line generates a png file os.system(‘dot -Tpng tree.dot -o tree.png’)
Straight from the documentation: [max_features] is the size of the random subsets of features to consider when splitting a node. So max_features is what you call m. When max_features=”auto”, m = p and no feature subset selection is performed in the trees, so the “random forest” is actually a bagged ensemble of ordinary regression trees. … Read more