7. Chi-Square Clearly Explained | Feature Selection Using Chi-Square | Chi-Square by hand |
#chisquaretest #statistics #featureselection #chi2 #datapreprocessing #machinelearning #python #scikitlearn #selectkbest #modelperformance #correlatedfeatures #datacleansing #datawrangling
Welcome to our video on feature selection using Chi-Square! In this tutorial, we'll be exploring a technique for identifying and selecting features that are highly correlated with the target variable. This method can be particularly useful when working with datasets that contain many irrelevant or redundant features.
We'll be demonstrating how to apply this technique in Python using scikit-learn. We'll start by loading and preparing the dataset, then we'll use scikit-learn's Chi2 method to calculate the Chi-Square statistic for each feature. Next, we'll use the SelectKBest method to select the top performing features based on their Chi-Square score. Finally, we'll evaluate the impact of feature selection on the model's performance.
By the end of this video, you'll have a solid understanding of how to use Chi-Square to select relevant features and improve the performance of your machine learning models.