random-forest

library(randomForest) rf.model <- randomForest(WIN ~ ., data = learn) I would like to fit a random forest model, but I get this error: Error in na.fail.default(list(WIN = c(2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, : missing values in object I have data frame learn with 16 numeric atributes and WIN is a factor with levels 0 1. My initial reaction to this question was that it didn't show much research effort, since "everyone" knows that random forests don't handle missing values in predictors. But upon checking ?randomForest I must confess that it could be much more explicit about this. (Although,

I'm using spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithSGD} and spark.mllib.tree.RandomForest for classification. Using these packages I produce classification models. Only these models predict a specific class per instance. In Weka, we can get the exact probability for each instance to be of each class. How can we do it using these packages? In LogisticRegressionModel we can set the threshold. So I've created a function that check the results for each point on a different threshold. But this cannot be done for RandomForest (see How to set cutoff while training

I have one question though. I heard from someone that in R, you can use extra packages to extract the decision rules implemented in RF, I try to google the same thing in python but without luck, if there is any help on how to achieve that. thanks in advance! Assuming that you use sklearn RandomForestClassifier you can find the invididual decision trees as .estimators_ . Each tree stores the decision nodes as a number of NumPy arrays under tree_ . Here is some example code which just prints each node in order of the array. In a typical application one would instead traverse by following the

How to print the decision path of a randomforest rather than the path of individual trees in a randomforest for a specific sample. import numpy as np import pandas as pd from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier X, y = make_classification(n_samples=1000, n_features=6, n_informative=3, n_classes=2, random_state=0, shuffle=False) # Creating a dataFrame df = pd.DataFrame({'Feature 1':X[:,0], 'Feature 2':X[:,1], 'Feature 3':X[:,2], 'Feature 4':X[:,3], 'Feature 5':X[:,4], 'Feature 6':X[:,5], 'Class':y}) y_train = df['Class'] X_train = df

parRF from the caret R package is not working for me with more than one core, which is quite ironic, given the par in parRF stands for parallel. I'm on a windows machine, if that is a relevant piece of information. I checked that I'm using the latest an greatest regarding caret and doParallel. I made a minimal example and and give the results below. Any ideas? Source code library(caret) library(doParallel) trCtrl <- trainControl( method = "repeatedcv" , number = 2 , repeats = 5 , allowParallel = TRUE ) # WORKS registerDoParallel(1) train(form = Species~., data=iris, trControl = trCtrl, method=

I want to evaluate a random forest being trained on some data. Is there any utility in Apache Spark to do the same or do I have to perform cross validation manually? zero323 ML provides CrossValidator class which can be used to perform cross-validation and parameter search. Assuming your data is already preprocessed you can add cross-validation as follows: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import org.apache.spark.ml.classification.RandomForestClassifier import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator