Database Reference
In-Depth Information
Summary
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 pre-
viously 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 mod-
els.
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