In this chapter, we covered the various classification models available in Spark MLlib, and we saw how to train models on input data, and how to evaluate their performance using standard metrics and measures. We also explored how to apply some of the techniques previously introduced to transform our features. Finally, we investigated the impact of using the correct input data format or distribution on model performance, and we also saw the impact of adding more data to our model, tuning model parameters and implementing cross-validation.
In the next chapter, we will take a similar approach to delve into MLlib's regression models.
