In the previous post, we explored and analyzed a customer churn data set. Then, we built a machine learning model to predict customer churn that achieved an accuracy of %91.7 on the training set and %90.7 on the test set.

In this post, we will work on:

• How to improve the accuracy (both on positive and negative class)
• How to lean the focus of the model more towards the positive class

It is important to note that the go-to way to increase the performance of a model is usually collecting more data. However, it may not always be an available option.

Let’s go back to our topic.

The model we built was a random forest classifier with hyperparameters:

• max_depth = 10 (Maximum depth of a tree in the forest)
• n_estimators = 200 (Number of trees in the forest)

#predictive-analytics #machine-learning #artificial-intelligence #programming #data-science