Regression Metrics in Machine Learning

Regression analysis is a machine learning technique used to predict continuous values. Once you have trained a regression model to predict Continuous values based on the problem you are solving, it is valuable to evaluate the model’s performance. So, if you want to know about the regression metrics you can use to evaluate the performance of your machine learning model, this article is for you. In this article, I will take you through the regression metrics in machine learning you should know.

Regression Metrics in Machine Learning

Below are some of the regression metrics you should know to evaluate the performance of your machine learning model:

1. Mean Squared Error
2. R2 Score
3. Bias and Variance

Let’s go through each of these regression metrics one by one.

Mean Squared Error (MSE)

Mean Squared Error (MSE) is a regression metric calculated by dividing the squared loss by the number of samples in the data. 

The mean squared error determines the distance between the set of points and the regression line by taking the distances from the points to the regression line and then swapping them. Distances are nothing but errors. Squaring is only done to remove negative values and to give more weight to higher differences.

If the MSE is smaller, you are very close to determining the best fit line (it also depends on the data you are working on, so sometimes it may not be possible to get a small MSE score).

You can learn how to calculate Mean Squared Error using Python here.

R2 Score

R2 Score is also known as the coefficient of determination. It works by measuring the amount of variance in the predictions explained by the dataset. Simply put, it is the difference between the samples in the dataset and the predictions made by the model.

If the value of the R2 Score is 1, it means that the model is perfect, and if its value is 0, it means that the model will not perform well on an unseen dataset. It also implies that the closer the value of the R2 Score is to 1, the more perfectly the model is trained.

You can learn the implementation of R2 Score using Python here.

Bias and Variance

Bias is the difference between predicted values and expected results. A machine learning model with a low bias is a perfect model, and a model with a high bias is expected with a high error rate on the training and test sets.

Variance is the variability of your model’s predictions over different datasets. A machine learning model with high variance indicates that the model may work well on the data it was trained on, but it will not generalize well on the dataset it has never seen before.

You can learn the implementation of bias and variance using Python here.

Summary

So these are some of the regression metrics that you should know about for evaluating the performance of your machine learning models. Here are some takeaways from this article:

1. Mean Squared Error (MSE) is a regression metric calculated by dividing the squared loss by the number of samples. 
2. R2 Score is the difference between the samples in the dataset and the predictions made by the model.
3. Bias is the difference between predicted values and expected results. Variance is the variability of your model’s predictions over different datasets.

I hope you liked this article on regression metrics in machine learning you should know. Feel free to ask valuable questions in the comments section below.