iqr quartiles[ quartiles[ (vw[ np median(vw)iqr out[ ] there are several other techniques for feature scaling and normalizationbut these should be sufficient to get you started and are used extensively in building machine learning systems always remember to check if you need to scale and standardize features whenever you are dealing with numerical features feature selection while it is good to try to engineering features that try to capture some latent representations and patterns in the underlying datait is not always good thing to deal with feature sets having maybe thousands of features or even more dealing with large number of features bring us to the concept of the curse of dimensionality which we mentioned earlier during the "bin countingsection in "feature engineering on categorical datamore features tend to make models more complex and difficult to interpret besides thisit can often lead to models over-fitting on the training data this basically leads to very specialized model tuned only to the data which it used for training and hence even if you get high model performanceit will end up performing very poorly on newpreviously unseen data the ultimate objective is to select an optimal number of features to train and build models that generalize very well on the data and prevent overfitting feature selection strategies can be divided into three main areas based on the type of strategy and techniques employed for the same they are described briefly as follows filter methodsthese techniques select features purely based on metrics like correlationmutual information and so on these methods do not depend on results obtained from any model and usually check the relationship of each feature with the response variable to be predicted popular methods include threshold based methods and statistical tests wrapper methodsthese techniques try to capture interaction between multiple features by using recursive approach to build multiple models using feature subsets and select the best subset of features giving us the best performing model methods like backward selecting and forward elimination are popular wrapper based methods embedded methodsthese techniques try to combine the benefits of the other two methods by leveraging machine learning models themselves to rank and score feature variables based on their importance tree based methods like decision trees and ensemble methods like random forests are popular examples of embedded methods the benefits of feature selection include better performing modelsless overfittingmore generalized modelsless time for computations and model trainingand to get good insight into understanding the importance of various features in your data in this sectionwe look at some of the most widely used techniques in feature selection you can load feature_selection py directly and start running the examples or use the jupyter notebookfeature selection ipynb for more interactive experience let' start by loading the following dependencies and configurations in [ ]import numpy as np import pandas as pd np set_printoptions(suppress=truept np get_printoptions()['threshold'