Measure Bias and Variance Using Various Machine Learning Models

This article was published as a part of the Data Science Blogathon. One of the most used matrices for measuring model performance is predictive errors. The components of any predictive errors are Noise, Bias, and Variance. This article intends to measure the bias and variance of a given model and observe the behavior of bias and variance w.r.t various models such as Linear Regression, Decision Tree, Bagging, and Random Forest for a given number of sample sizes. Bias: Difference between the prediction of the true model and the average models (models build on n number of samples obtained from the population).