Types of Chi-Square test

What is Chi-Square test?

The chi-square test is a statistical test used to determine if there is a significant association between two categorical variables. It is a non-parametric test, meaning it makes no assumptions about the distribution of the data. The test is based on the comparison of observed and expected frequencies within a contingency table. The chi-square test helps with feature selection problems by looking at the relationship between the elements. It determines if the association between two categorical variables of the sample would reflect their real association in the population....

Types of Chi-Square test

There are several types of chi-square tests, each designed to address specific research questions or scenarios. The two main types are the chi-square test for independence and the chi-square goodness-of-fit test....

Why do we use the Chi-Square Test?

The chi-square test is widely used across diverse fields to analyze categorical data, offering valuable insights into associations or differences between categories.Its primary application lies in testing the independence of two categorical variables, determining if changes in one variable relate to changes in another.It is particularly useful for understanding relationships between factors, such as gender and preferences or product categories and purchasing behaviors.Researchers appreciate its simplicity and ease of application to categorical data, making it a preferred choice for statistical analysis.The test provides insights into patterns and associations within categorical data, aiding in the interpretation of relationships.Its utility extends to various fields, including genetics, market research, quality control, and social sciences, showcasing its broad applicability.The chi-square test helps assess the conformity of observed data to expected values, enhancing its role in statistical analysis....

Steps to perform Chi-square test

Define Null Hypothesis (H0): There is no significant association between the two categorical variables.Alternative Hypothesis (H1): There is a significant association between the two categorical variables.Create a contingency table that displays the frequency distribution of the two categorical variables.Find the Expected values using formula: ………..eq(2)where, : Totals of row i: Totals of column jN: Total number of ObservationsCalculate the Chi-Square StatisticDegrees of Freedom using formula:………..eq(3)where, m corresponds to the number of categories in one categorical variable.n corresponds to the number of categories in another categorical variable.Accept or Reject the Null Hypothesis: Compare the calculated chi-square statistic to the critical value from the chi-square distribution table for the chosen significance level (e.g., 0.05)If is greater than the critical value, reject the null hypothesis, indicating a significant association between the variables.If is less than or equal to the critical value, fail to reject the null hypothesis, suggesting no significant association....

Chi-square Test for Feature Selection

Chi-square test is used for categorical features in a dataset. We calculate Chi-square between each feature and the target and select the desired number of features with best Chi-square scores. Features that show significant dependencies with the target variable are considered important for prediction and can be selected for further analysis....

Python Implementation of Chi-Square feature selection

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Conclusion

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Frequently Asked Questions (FAQs)

Python3 import pandas as pd from sklearn.datasets import load_iris from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2   # Load the dataset iris = load_iris() X = iris.data y = iris.target   # Converting to DataFrame for better visualization column_names = [f'feature_{i}' for i in range(X.shape[1])] df = pd.DataFrame(X, columns=column_names) df['target'] = y   print("Original Dataset:") print(df.head())   # Applying Chi-Square feature selection and # Selecting top k features k = 2 chi2_selector = SelectKBest(chi2, k=k) X_new = chi2_selector.fit_transform(X, y)   selected_features = df.columns[:-1][chi2_selector.get_support()] print("\nSelected Features:") print(selected_features)...