Performance Evaluation Metrics in Machine Learning

Some of the best and easy to understand performance evaluation metrics in machine learning.

In Machine Learning, the performance evaluation metrics are used to calculate the performance of your trained machine learning models. This helps in finding how better your machine learning model can perform on a dataset that it has never seen before. If you have never used any performance evaluation metrics to evaluate the performance of your machine learning model, then this article is for you. In this article, I will take you through an introduction to some of the best performance evaluation metrics in machine learning.

Performance Evaluation Metrics

In machine learning, a performance evaluation metric plays a very important role in determining the performance of our machine learning model on a dataset that it has never seen before. Chances are, the model you have trained will always perform better on the dataset you have trained it. But we train machine learning models to perform well while solving real-world problems where data flows continuously. If we are using a model that is not capable enough to perform well, there is no point in using machine learning to solve your problems. This is where performance evaluation metrics come in. A performance evaluation metric calculates whether your trained machine learning model will perform well in solving the problem it was trained for or not.

There are many performance evaluation metrics that you can use to measure the performance of your machine learning models for classification as well as for regression. Below are some of the best performance measurement metrics that I will recommend you use to assess the performance of your machine learning model.

R2 Score:

The R2 score is a very important metric that is used to evaluate the performance of a regression-based machine learning model. It is pronounced as R squared and 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. You can find a tutorial on it from here.

Explained Variance:

The explained variance is used to measure the proportion of the variability of the predictions of a machine learning model. Simply put, it is the difference between the expected value and the predicted value. The concept of explained variance is very important in understanding how much information we can lose by reconciling the dataset. You can find a tutorial on it from here.

Confusion matrix:

The confusion matrix is a method of evaluating the performance of a classification model. The idea behind this is to count the number of times instances of class 1 are classified as class 2. For example, to find out how many times the classification model has confused the images of Dog with Cat, you use the confusion matrix. You can find a tutorial on it from here.

Classification Report:

A classification report is one of the performance evaluation metrics of a classification-based machine learning model. It displays your model’s precision, recall, F1 score and support. It provides a better understanding of the overall performance of our trained model. To understand the classification report of a machine learning model, you need to know all of the metrics displayed in the report. For a clear understanding, I have explained all of the metrics below so that you can easily understand the classification report of your machine learning model:

1. Precision: Precision is defined as the ratio of true positives to the sum of true and false positives.
2. Recall: Recall is defined as the ratio of true positives to the sum of true positives and false negatives.
3. F1 score: The F1 is the weighted harmonic mean of precision and recall. The closer the value of the F1 score is to 1.0, the better the expected performance of the model is.
4. Support: Support is the number of actual occurrences of the class in the dataset. It doesn’t vary between models, it just diagnoses the performance evaluation process.

You can find a tutorial on it from here.

Summary

So these are some of the best performance evaluation metrics that you can use to measure the performance of your machine learning model. In machine learning, a performance evaluation metric plays a very important role in determining the performance of our machine learning model on a dataset that it has never seen before. I hope you liked this article on performance evaluation metrics in machine learning. Feel free to ask your valuable questions in the comments section below.