What is chi-square test for feature selection?
A chi-square test is used in statistics to test the independence of two events. Given the data of two variables, we can get observed count O and expected count E. Chi-Square measures how expected count E and observed count O deviates each other.
How do you select features in machine learning?
Feature Selection: Select a subset of input features from the dataset.
- Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.
- Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE.
How is the chi square test useful in machine learning?
We always wonder where the Chi-Square test is useful in machine learning and how this test makes a difference. Feature selection is an important problem in machine learning, where we will be having several features in line and have to select the best features to build the model.
How to test chi square feature selection in Python?
Python Implementation of Chi-Square feature selection: Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready. Writing code in comment?
How is feature selection used in machine learning?
Feature selection is also known as attribute selection is a process of extracting the most relevant features from the dataset and then applying machine learning algorithms for the better performance of the model. A large number of irrelevant features increases the training time exponentially and increase the risk of overfitting.
What does expected frequency mean in chi square?
Expected frequency = No. of expected observations of class if there was no relationship between the feature and the target. Python Implementation of Chi-Square feature selection: Attention reader! Don’t stop learning now.