Random forest model

In this exercise, you will use the randomForest() function in the randomForest package to build a random forest model for predicting churn of the customers in the training data set, training_set. The target variable is called Future. You will also inspect and visualize the importance of the variables in the model.

Bu egzersiz

Predictive Analytics using Networked Data in R

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Load the randomForest package.
Use the set.seed() function with the seed 863.
Build a random forest using the function randomForest() and all the variables in training_set. The response variable Future needs to be a factor, so utilize the as.factor() function.
Plot the variable importance of the random forest model using varImpPlot().

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Load package
___(randomForest)

# Set seed
set.seed(___)

# Build model
rfModel <- ___(as.factor(___)~. ,data=training_set)

# Plot variable importance
varImpPlot(___)

Kodu Düzenle ve Çalıştır