random-forest

I have two RandomForestClassifier models, and I would like to combine them into one meta model. They were both trained using similar, but different, data. How can I do this? rf1 #this is my first fitted RandomForestClassifier object, with 250 trees rf2 #this is my second fitted RandomForestClassifier object, also with 250 trees I want to create big_rf with all trees combined into one 500 tree model I believe this is possible by modifying the estimators_ and n_estimators attributes on the RandomForestClassifier object. Each tree in the forest is stored as a DecisionTreeClassifier object, and

I'm trying to extract the feature importances of a random forest object I have trained using PySpark. However, I do not see an example of doing this anywhere in the documentation, nor is it a method of RandomForestModel. How can I extract feature importances from a RandomForestModel regressor or classifier in PySpark? Here's the sample code provided in the documentation to get us started; however, there is no mention of feature importances in it. from pyspark.mllib.tree import RandomForest from pyspark.mllib.util import MLUtils # Load and parse the data file into an RDD of LabeledPoint. data =

I am new to numpy and I am implementing clustering with random forest in python. My question is: How could I find the index of the exact row in an array? For example [[ 0. 5. 2.] [ 0. 0. 3.] [ 0. 0. 0.]] and I look for [0. 0. 3.] and get as result 1(the index of the second row). Any suggestion? Follows the code (not working...) for index, element in enumerate(leaf_node.x): for index_second_element, element_two in enumerate(leaf_node.x): if (index <= index_second_element): index_row = np.where(X == element) index_column = np.where(X == element_two) self.similarity_matrix[index_row][index_column

I'm running GridSearch CV to optimize the parameters of a classifier in scikit. Once I'm done, I'd like to know which parameters were chosen as the best. Whenever I do so I get a AttributeError: 'RandomForestClassifier' object has no attribute 'best_estimator_' , and can't tell why, as it seems to be a legitimate attribute on the documentation . from sklearn.grid_search import GridSearchCV X = data[usable_columns] y = data[target] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50

I have a dataset where the classes are unbalanced. The classes are either '1' or '0' where the ratio of class '1':'0' is 5:1. How do you calculate the prediction error for each class and the rebalance weights accordingly in sklearn with Random Forest, kind of like in the following link: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#balance alko 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