Feature selection
Feature selection is a technique that involves reducing the number of features in the machine learning process while leveraging lesser data and also improving the accuracy of the trained model. Feature selection is the process of either automatically or manually selecting only those features that contribute the most to the prediction variable that you are interested in. Feature selection is an important aspect of machine learning, as irrelevant or semi-relevant features can gravely impact model accuracy.
Apache Spark MLlib comes packaged with a few feature selectors, including VectorSlicer, ChiSqSelector, UnivariateFeatureSelector, and VarianceThresholdSelector. Let's explore how to implement feature selection within Apache Spark using the following code example that utilizes ChiSqSelector to select the optimal features given the label column that we are trying to predict:
from pyspark.ml.feature import ChiSqSelector chisq_selector=ChiSqSelector(numTopFeatures...