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  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

Setting up GridSearch parameters

A hyperparameter is a parameter inside a function. For example, max_depth or min_samples_leaf are hyperparameters of the DecisionTreeClassifier() function. Hyperparameter tuning is the process of testing different values of hyperparameters to find the optimal ones: the one that gives the best predictions according to your objectives. In sklearn, you can use GridSearch to test different combinations of hyperparameters. Even better, you can use GridSearchCV() to test different combinations and run cross-validation on them in one function!

In this exercise, you are going to prepare the different values you want to test for max_depth and min_samples_leaf. You will then put these in a dictionary, because that’s what is required for GridSearchCV():

  • the dictionary keys will be the hyperparameters names
  • the dictionary values will be the attributes (the hyperparameter values) you want to test

Instead of writing all the values manually, you will use the range() function, which allows us to generate values incrementally. For example, range(1, 10, 2) will generate a list containing values ranging from 1 included to 10 not included, by increments of 2. So the final result will be [1, 3, 5, 7, 9].

Instructions

100 XP
  • Following the format in the example above, generate values for the maximum depth ranging from 5 to 20 with increments of 1
  • Do the same for the minimum sample size with values from 50 to 450 with increments of 50
  • Create the dictionary by specifying the max_depth and min_samples_leaf values to try, respective values, using the variables you just created