Mastering Predictive Analytics with Python

Summary

In this chapter, you've examined how to use classification models and some of the strategies for improving model performance. In addition to transforming categorical features, you've looked at the interpretation of logistic regression accuracy using the ROC curve. In attempting to improve model performance, we demonstrated the use of SVMs and were able to increase performance on the training set the cost of overfitting. Finally, we were able to achieve good performance on the test set through gradient boosted decision trees. Taken together with the material in Chapter 4, Connecting the Dots with Models – Regression Methods, you should now have a full toolkit of methods for continuous and categorical outcomes, which you can apply to problems ...