What is bias?

What is bias?

Bias is the disparity between the predictions made by a machine learning model and the actual value, often leading to significant errors in both training and testing data. Algorithms must have low bias to prevent underfitting. High bias results in predictions that follow a simplistic, linear pattern, failing to accurately represent the complexity of the dataset. This scenario is known as underfitting, where the hypothesis is too basic or linear....

What is variance?

Variance in the context of machine learning refers to the variability of model predictions for a given data point. A model with high variance has a complex fit to the training data, which can lead to overfitting. This means that the model performs well on the training data but has high error rates on test data because it has not generalized well to unseen data. To address high variance, it’s important to keep the variance low by using techniques such as regularization and cross-validation to prevent overfitting...

What is bias-variance tradeoff?

The bias-variance tradeoff is a fundamental concept in machine learning that deals with the balance between two types of errors that a model can make: bias and variance. It refers to the tradeoff between a model’s ability to accurately capture the underlying patterns in the data (low bias) and its tendency to be sensitive to variations in the training data (high variance)....

How to overcome Bias-Variance Tradeoff?

To overcome the bias-variance tradeoff, several strategies can be employed:...

Bias-Variance Tradeoff Using Python

To show the bias-variance tradeoff using Python, we can create a simple example using polynomial regression. We’ll generate some synthetic data and fit polynomial models of different degrees to observe how bias and variance change with model complexity....