Regression in Python

When using classification models in machine learning, there are three common metrics that we use to assess the quality of the model:…

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…

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 likelihood ratio test compares the goodness of fit of two nested regression models. A nested model is simply one that contains…

White’s test is used to determine if heteroscedasticity is present in a regression model. Heteroscedasticity refers to the unequal scatter of residuals…

We often use three different sum of squares values to measure how well a regression line fits a dataset: 1. Sum of…

Matthews correlation coefficient (MCC) is a metric we can use to assess the performance of a classification model. It is calculated as:…

Balanced accuracy is a metric we can use to assess the performance of a classification model. It is calculated as: Balanced accuracy…

You can use the following basic syntax to plot a line of best fit in Python: #find line of best fit a,…

One-hot encoding is used to convert categorical variables into a format that can be readily used by machine learning algorithms. The basic…