1. Learn
    2. /
  2. Courses
    3. /
  3. HR Analytics: Predicting Employee Churn in Python
  • 1

    Introduction to HR Analytics

    Free

    In this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.

  • 2

    Predicting employee turnover

    This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.

  • 3

    Evaluating the turnover prediction model

    Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.

  • 4

    Choosing the best turnover prediction model

    In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!

Initializing

Exercise

Pruning the tree

Overfitting is a classic problem in analytics, especially for the decision tree algorithm. Once the tree is fully grown, it may provide highly accurate predictions for the training sample, yet fail to be that accurate on the test set. For that reason, the growth of the decision tree is usually controlled by:

  • “Pruning” the tree and setting a limit on the maximum depth it can have.
  • Limiting the minimum number of observations in one leaf of the tree.

In this exercise, you will:

  • prune the tree and limit the growth of the tree to 5 levels of depth
  • fit it to the employee data
  • test prediction results on both training and testing sets.

The variables features_train, target_train, features_test and target_test are already available in your workspace.

Instructions

100 XP
  • Initialize the DecisionTreeClassifier while limiting the depth of the tree to 5.
  • Fit the Decision Tree model using the features and the target in the training set.
  • Check the accuracy of the predictions on both the training and test sets.