# How to get Regression R_squared

R-squared is a statistical measure that helps us understand how well a regression model is able to explain the variation in the target variable. It is a value between 0 and 1, where a value closer to 1 indicates a better fit of the model to the data.

To calculate R-squared, we first need to have a dataset with both the predicted values and the actual values. We then calculate the total sum of squares (SST) which is the sum of the squared differences between the actual values and the mean of the actual values. Next, we calculate the residual sum of squares (SSR) which is the sum of the squared differences between the predicted values and the actual values. R-squared is calculated by taking 1 minus (SSR/SST).

R-squared is a useful measure to evaluate the model, but it has some limitations. For example, it does not indicate whether the coefficient estimates and predictions are biased, or whether the model is overfitting or underfitting the data.

It is important to note that R-squared value alone can’t tell you how good the model is, it should be accompanied by other evaluation metrics and visualizations. Also, if your model is overfitting, having high R-squared value is not a good indication of the model’s performance.

In summary, R-squared is a measure of how well a regression model is able to explain the variation in the target variable. It is a value between 0 and 1, and a value closer to 1 indicates a better fit of the model to the data. It is important to use R-squared in conjunction with other evaluation metrics and visualizations to properly evaluate the performance of a regression model.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Regression R_squared.

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