Goodness Of Fit
Goodness of fit is a statistical measure that determines how well a model fits the observed data. It is used to evaluate the accuracy of a model by comparing the expected values of a variable to the actual values. The goodness of fit test is used to determine whether the differences between the observed and expected values are due to chance or whether the model needs to be revised. The most commonly used goodness of fit test is the chi-square test, which compares the observed and expected frequencies of a categorical variable. Other measures of goodness of fit include the coefficient of determination (R-squared) and the root mean square error (RMSE) for continuous variables. Goodness of fit is an important concept in data science as it helps to determine the validity of a model and its ability to make accurate predictions.
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