Creating the model
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To demonstrate the application of the decision tree model, we will consider the Churn data set. Our aim is to be able to predict whether a customer will Churn or not, using a Decision Tree classifier.
Before creating the model, we have included a few pre-processing steps.
Firstly, we have adjusted for missing values in the data by replacing with the mode for discrete (categorical) columns and mean for continuous (numeric) columns.
Secondly, to make the interpretation easy, we have reduced the data set using feature importance. In this step, we have essentially trained a Decision Tree classifier on the entire data set, and then calculated the feature importance for every column using the "model.feature_importances_" function (inbuilt function).
After calculating the feature importance for every column, we have filtered out the columns that had a value lower than our threshold value. The threshold value chosen by us is 0.05, but this can be changed as per your convenience.
Having filtered the unimportant columns, we remain with only four columns - Monthly Charges, Total Charges, Contract and Tenure. We have used these four columns to train our model.
After running the model with the best hyper-parameters from Grid Search, we get an accuracy of 76% on the test set. This is actually pretty good! Especially because trees generally tend to overfit on training data and don't perform well on test data.
One last step before we train our model is to ensure we use the right hyperparameters. There are multiple ways to find the right hyperparameters that would yield a highly accurate model. In our case, we have used Grid Search to find them. If you are interested in knowing how the Grid Search algorithms works, read this .
