Regression in Python
Python
How to Interpret the Classification Report in sklearn (With Example)
When using classification models in machine learning, there are three common metrics that we use to assess the quality of the model:
1. Precision: Percentage...
Python
How to Get Regression Model Summary from Scikit-Learn
Often you may want to extract a summary of a regression model created using scikit-learn in Python.
Unfortunately, scikit-learn doesn’t offer many built-in functions to...
Python
How to Calculate R-Squared in Python (With Example)
R-squared, often written R2, is the proportion of the variance in the response variable that can be explained by the predictor variables in a linear...
Python
How to Perform a Likelihood Ratio Test in Python
A likelihood ratio test compares the goodness of fit of two nested regression models.
A nested model is simply one that contains a subset of...
Python
How to Perform White’s Test in Python (Step-by-Step)
White’s test is used to determine if heteroscedasticity is present in a regression model.
Heteroscedasticity refers to the unequal scatter of residuals at different levels...
Python
How to Calculate SST, SSR, and SSE in Python
We often use three different sum of squares values to measure how well a regression line fits a dataset:
1. Sum of Squares Total (SST)...
Python
How to Calculate Balanced Accuracy in Python Using sklearn
Balanced accuracy is a metric we can use to assess the performance of a classification model.
It is calculated as:
Balanced accuracy = (Sensitivity + Specificity)...
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