MLlib also offers a wide range of classifiers; it provides both binomial and multinomial logistic regressor. The decision tree classifier, random forest classifier, gradient-boosted tree classifier, multilayered perceptron classifier, linear support vector machine classifier, and Naive Bayes classifier are supported. Each of them is defined in its class; for details, refer to https://spark.apache.org/docs/2.2.0/ml-classification-regression.html. The basic steps remain the same as we learned in the case of regression; the only difference is now, instead of RMSE or r2 metrics, the models are evaluated on accuracy.
This section will treat you to the wine quality classification problem implemented using Spark MLlib logistic ...
