Summary

In this chapter, we learned about the SVM, KNN, and Random Forest classification algorithms and applied them to our preprocessed Human Resource Analytics dataset to build predictive models. These models were trained to predict whether an employee will leave the company, given a set of employee metrics.

For the purposes of keeping things simple and focusing on the algorithms, we built models that depend on only two features, that is, the satisfaction level and last evaluation value. This two-dimensional feature space also allowed us to visualize the decision boundaries and identify what overfitting looks like.

In the next chapter, we will introduce two important topics in machine learning: k-fold cross validation and validation curves. In doing so, we'll discuss more advanced topics, such as parameter tuning and model selection. Then, to optimize our final model for the employee retention problem, we'll explore feature extraction with the dimensionality reduction...