In this chapter, we discovered classification, its techniques, the train-test split strategy, and performance evaluation measures. This will benefit you in gaining an important skill for predictive data analysis. You have seen how to develop linear and non-linear classifiers for predictive analytics using scikit-learn. In the earlier topics of the chapter, you got an understanding of the basics of classification and machine learning algorithms, such as naive Bayes classification, decision tree classification, KNN, and SVMs. In later sections, you saw data splitting approaches and model performance evaluation measures such as accuracy score, precision score, recall score, F1-score, ROC curve, and AUC score.
The next chapter, Chapter 11, Unsupervised Learning – PCA and Clustering, will concentrate on the important topics of unsupervised machine learning techniques and dimensionality reduction techniques in Python. The chapter starts with dimension reduction and principal...