In this chapter, you have learned how the logistic regression model works on a mathematical level. Although simplistic, the model proves to be formidable in terms of interpretability, which is highly beneficial in the financial industry.
You have also learned how to build and evaluate logistic regression algorithms using scikit-learn, and looked at hyperparameter optimization using the GridSearchCV algorithm. Additionally, you have learned to verify whether the results provided to you by the GridSearchCV algorithm are accurate by plotting the accuracy scores for different values of the hyperparameter.
Finally, you have scaled your data in order make it standardized and learned how to interpret your model on a mathematical level.
In the next chapter, you will learn how to implement tree-based algorithms, such as decision trees, random forests, and gradient-boosted trees...