random-forest

Does anyone have any advice about how to convert the tree information from sparklyr's ml_decision_tree_classifier, ml_gbt_classifier, or ml_random_forest_classifier models into a.) a format that can be understood by other R tree-related libraries and (ultimately) b.) a visualization of the trees for non-technical consumption? This would include the ability to convert back to the actual feature names from the substituted string indexing values that are produced during the vector assembler. The following code is copied liberally from a sparklyr blog post for the purposes of providing an example:

I'm trying a random forest classification model by using H2O library inside R on a training set having 70 million rows and 25 numeric features.The total file size is 5.6 GB. The validation file's size is 1 GB. I have 16 GB RAM and 8 core CPU on my system. The system successfully able to read both of the files in H2O object. Then I'm giving below command to build the model: model <- h2o.randomForest(x = c(1:18,20:25), y = 19, training_frame = traindata, validation_frame = testdata, ntrees = 150, mtries = 6) But after few minutes (without generating any tree), I'm getting following error: "Error

In the documentation of SciKit-Learn Random Forest classifier , it is stated that The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default). What I dont understand is that if the sample size is always the same as the input sample size than how can we talk about a random selection. There is no selection here because we use all the (and naturally the same) samples at each training. Am I missing something here? Lol4t0 I believe this part of docs answers your question In random forests (see RandomForestClassifier

I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. Currently, the train set has about 2 years data. Is there a way to train on another 2 years and (kind of) append it to the existing saved model. rf = RandomForestRegressor(n_estimators=100) print ("Trying to fit the Random Forest model --> ") if os.path.exists('rf.pkl'): print ("Trained model already pickled -- >") with open('rf.pkl', 'rb') as f: rf = cPickle.load(f) else: df_x_train = x_train[col_feature] rf.fit(df_x_train,y_train)

I have used the RandomForestClassifier in sklearn for determining the important features in my dataset. How am I able to return the actual feature names (my variables are labeled x1, x2, x3, etc.) rather than their relative name (it tells me the important features are '12', '22', etc.). Below is the code that I am currently using to return the important features. important_features = [] for x,i in enumerate(rf.feature_importances_): if i>np.average(rf.feature_importances_): important_features.append(str(x)) print important_features Additionally, in an effort to understand the indexing, I was

I've built a pipeline in Scikit-Learn with two steps: one to construct features, and the second is a RandomForestClassifier. While I can save that pipeline, look at various steps and the various parameters set in the steps, I'd like to be able to examine the feature importances from the resulting model. Is that possible? Ah, yes it is. You list identify the step where you want to check the estimator: For instance: pipeline.steps[1] Which returns: ('predictor', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None,