R squared and p value in Logistic regression | in Urdu/Hindi
Channel: Codanics
Date: 2022-12-20

#python #datascience #machinelearning 

In logistic regression, R-squared and p-value are two commonly used measures to evaluate the fit of a model.

R-squared, also known as the coefficient of determination, is a measure of how well the model fits the data. It is a value between 0 and 1, with higher values indicating a better fit. In logistic regression, R-squared is not typically used to evaluate the fit of the model because it is not directly interpretable in this context.

P-value, on the other hand, is a statistical measure that is used to determine the probability of obtaining a certain result, given that a certain assumption is true. In logistic regression, the p-value is used to determine the statistical significance of each predictor variable. A p-value less than 0.05 indicates that the predictor is statistically significant and likely to be important in predicting the outcome.

Overall, R-squared and p-value are important measures to consider when evaluating the fit and significance of a logistic regression model. Understanding these measures can help you make informed decisions about the results of your analysis and the implications for your research or business problem.

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