You started the chapter with understanding predictive analysis with classification and explored the concepts of training, testing, and validating a classification model. You then proceeded to carry on building experiments with different two-class and multiclass classification models, such as logistic regression, decision forest, neural network, and boosted decision trees inside ML Studio. You learned how to score and evaluate a model after training. You also learned how to optimize different parameters for a learning algorithm by the module, Sweep Parameters.

After exploring the two-class classification, you understood multiclass classification and learnt how to evaluate a model for the same. You then built a couple of models for multiclass classification using different available algorithms.

In the next chapter, you will explore the process of building a model using clustering, an unsupervised learning algorithm.