random-forest

Do you guys know where can I find examples of multiclass classification in Spark. I spent a lot of time searching in books and in the web, and so far I just know that it is possible since the latest version according the documentation. zero323 ML ( Recommended in Spark 2.0+ ) We'll use the same data as in the MLlib below. There are two basic options. If Estimator supports multilclass classification out-of-the-box (for example random forest) you can use it directly: val trainRawDf = trainRaw.toDF import org.apache.spark.ml.feature.{Tokenizer, CountVectorizer, StringIndexer} import org.apache

I am trying to use quantile regression forest function in R ( quantregForest ) which is built on Random Forest package. I am getting a type mismatch error that I can't quite figure why. I train the model by using qrf <- quantregForest(x = xtrain, y = ytrain) which works without a problem, but when I try to test with new data like quant.newdata <- predict(qrf, newdata= xtest) it gives the following error: Error in predict.quantregForest(qrf, newdata = xtest) : Type of predictors in new data do not match types of the training data. My training and testing data are coming from separate files

Say I have a categorical feature, color, which takes the values ['red', 'blue', 'green', 'orange'], and I want to use it to predict something in a random forest. If I one-hot encode it (i.e. I change it to four dummy variables), how do I tell sklearn that the four dummy variables are really one variable? Specifically, when sklearn is randomly selecting features to use at different nodes, it should either include the red, blue, green and orange dummies together, or it shouldn't include any of them. I've heard that there's no way to do this, but I'd imagine there must be a way to deal with

I am using random forests in a big data problem, which has a very unbalanced response class, so I read the documentation and I found the following parameters: strata sampsize The documentation for these parameters is sparse (or I didn´t have the luck to find it) and I really don´t understand how to implement it. I am using the following code: randomForest(x=predictors, y=response, data=train.data, mtry=lista.params[1], ntree=lista.params[2], na.action=na.omit, nodesize=lista.params[3], maxnodes=lista.params[4], sampsize=c(250000,2000), do.trace=100, importance=TRUE) The response is a class

I am currently using H2O for a classification problem dataset. I am testing it out with H2ORandomForestEstimator in a python 3.6 environment. I noticed the results of the predict method was giving values between 0 to 1(I am assuming this is the probability). In my data set, the target attribute is numeric i.e. True values are 1 and False values are 0. I made sure I converted the type to category for the target attribute, I was still getting the same result. Then I modified to the code to convert the target column to factor using asfactor() method on the H2OFrame still, there wasn't any change

I learn the machine learning with the scikit-learn library. I apply the decision tree classifier and the random forest classifier to my data with this code: def decision_tree(train_X, train_Y, test_X, test_Y): clf = tree.DecisionTreeClassifier() clf.fit(train_X, train_Y) return clf.score(test_X, test_Y) def random_forest(train_X, train_Y, test_X, test_Y): clf = RandomForestClassifier(n_estimators=1) clf = clf.fit(X, Y) return clf.score(test_X, test_Y) Why the result are so much better for the random forest classifier (for 100 runs, with randomly sampling 2/3 of data for the training and 1/3