random-forest

I am trying to find Accuracy using 5-fold cross validation using Random Forest Classifier Model in SCALA. But i am getting the following error while running: java.lang.IllegalArgumentException: RandomForestClassifier was given input with invalid label column label, without the number of classes specified. See StringIndexer. Getting the above error at line---> val cvModel = cv.fit(trainingData) The code which i used for cross validation of data set using random forest is as follows: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import

I have built an Random Forest model and I got two different prediction results when I wrote two different lines of code in order to generate the prediction. I wonder which one is the right one. Here is my example dataframe and the usedcode: dat <- read.table(text = " cats birds wolfs snakes 0 3 9 7 1 3 8 4 1 1 2 8 0 1 2 3 0 1 8 3 1 6 1 2 0 6 7 1 1 6 1 5 0 5 9 7 1 3 8 7 1 4 2 7 0 1 2 3 0 7 6 3 1 6 1 1 0 6 3 9 1 6 1 1 ",header = TRUE) I've built a random forest model: model<-randomForest(snakes~cats+birds+wolfs,data=dat,ntree=20) RF_pred<- data.frame(predict(model)) train<-cbind(train,RF_pred) #

I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Attribute leaf is the predicted value. In other words, if the evaluation of a tree model ends at that terminal node (aka leaf node), then this is the value that is returned. In pseudocode (the left-most branch of your tree model): if(f1 < 127.5){ if(f7 < 28.5){ if(f5 < 45.4){ return 0

I'm dealing for the first time with random forests and I'm having some troubles that I can't figure out.. When I run the analysis on all my dataset (about 3000 rows) I don't get any error message. But when I perform the same analysis on a subset of my dataset (about 300 rows) I get an error: dataset <- read.csv("datasetNA.csv", sep=";", header=T) names (dataset) dataset2 <- dataset[complete.cases(dataset$response),] library(randomForest) dataset2 <- na.roughfix(dataset2) data.rforest <- randomForest(dataset2$response ~ dataset2$predictorA + dataset2$predictorB+ dataset2$predictorC + dataset2

I am very new at machine learning and am attempting the forest cover prediction competition on Kaggle , but I am getting hung up pretty early on. I get the following error when I run the code below. Error in train.default(x, y, weights = w, ...) : final tuning parameters could not be determined In addition: There were 50 or more warnings (use warnings() to see the first 50) # Load the libraries library(ggplot2); library(caret); library(AppliedPredictiveModeling) library(pROC) library(Amelia) set.seed(1234) # Load the forest cover dataset from the csv file rawdata <- read.csv("train.csv"

I'm trying to implement the FBeta_Score() of the MLmetrics R package : FBeta_Score <- function(y_true, y_pred, positive = NULL, beta = 1) { Confusion_DF <- ConfusionDF(y_pred, y_true) if (is.null(positive) == TRUE) positive <- as.character(Confusion_DF[1,1]) Precision <- Precision(y_true, y_pred, positive) Recall <- Recall(y_true, y_pred, positive) Fbeta_Score <- (1 + beta^2) * (Precision * Recall) / (beta^2 * Precision + Recall) return(Fbeta_Score) } in the H2O distributed random forest model and I want to optimize it during the training phase using the custom_metric_func option. The help

I'm trying to use cforest function(R, party package). This's what I do to construct forest: library("party") set.seed(42) readingSkills.cf <- cforest(score ~ ., data = readingSkills, control = cforest_unbiased(mtry = 2, ntree = 50)) Then I want to print the first tree and I do party:::prettytree(readingSkills.cf@ensemble[[1]],names(readingSkills.cf@data@get("input"))) The result look like this 1) shoeSize <= 28.29018; criterion = 1, statistic = 89.711 2) age <= 6; criterion = 1, statistic = 48.324 3) age <= 5; criterion = 0.997, statistic = 8.917 4)* weights = 0 3) age > 5 5)* weights = 0 2)