Regression Metrics

It’s important to know algorithms for regression, but it’s equally important to know how to evaluate them properly. Otherwise, we cannot properly choose between two different models.

The first step is to learn the main evaluation metrics, which will answer the following question: is this model any good?

Regression metrics

Regression metrics are vital tools in the field of data analysis and ML, offering insights into the performance of predictive models that focus on continuous numeric outcomes. We’ll delve into various evaluation techniques and metrics used to assess the accuracy and efficacy of regression models. By employing these metrics, we can gauge the quality of our predictions and make informed decisions regarding model selection, fine-tuning, and deployment.

The key regression metrics we’ll see include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, R-squared (R2). Each of these metrics serves a unique purpose in assessing the performance of regression models, with MSE and RMSE emphasizing the magnitude of prediction errors, MAE focusing on their absolute values, and R2 providing a measure of the variance explained by the model.

We’ll explore these metrics in detail, shedding light on their strengths, weaknesses, and best use cases to develop the knowledge required to effectively evaluate and optimize regression models.

Mean squared error (MSE)

The mean squared error (MSE) is a popular metric for evaluating the performance of regression models. It measures the average squared difference between the predicted and actual values: