When training a machine learning model, it is very important to understand the bias and variance of predictions of your model. It helps in analyzing prediction errors which help us in training more accurate machine learning models. In this article, I’ll walk you through how to calculate bias and variance using Python.
What are 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 sets of data. 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.
Bias and Variance using Python
Hope you now have understood what bias and variance are in machine learning and how a model with high bias and variance can affect your model’s performance on a dataset that it has never seen before. Now in this section, I will walk you through a tutorial on how to calculate bias and variance using Python.
You must be using the scikit-learn library in Python for implementing most of the machine learning algorithms. But it does not have any function to calculate the bias and variance of your trained model. So to calculate the bias and variance of your model using Python, you have to install another library known as mlxtend. You can easily install it in your system by using the pip command:
- pip install mlxtend
Now let’s train a machine learning model and then we will see how we can calculate its bias and variance using Python:
So till now, we have trained a machine learning model by using the linear regression algorithm, below is how we can calculate its bias and variance using Python:
Average Bias : 3.909459558063484 Average Variance : 0.07349200663859749
Summary
Bias is the difference between predicted values and expected results. Variance is the variability of your model’s predictions over different sets of data. I hope you liked this article on how to calculate the bias and variance of a machine learning model. Feel free to ask your valuable questions in the comments section below.
