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.

Deze oefening maakt deel uit van de cursus

Predictive Analytics using Networked Data in R

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Oefeninstructies

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().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load package
___(randomForest)

# Set seed
set.seed(___)

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

# Plot variable importance
varImpPlot(___)

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