What is bias-variance trade-off?
What is Bias?
Bias is the simplifying assumptions made by a model to make the target function easier to learn.
- Low Bias : Predicting less assumption about Target Function
- High Bias: Predicting more assumption about Target Function
Examples of low-bias machine learning algorithms include Decision Trees, k-Nearest Neighbors and Support Vector Machines.
Examples of high-bias machine learning algorithms include Linear Regression, Linear Discriminant Analysis, and Logistic Regression.
What is Variance?
Variance is the amount that the estimate of the target function will change if different training data was used.
- Low Variance : Predicting small changes to the estimate of the target function with changes to the training dataset.
- High Variance : Predicting large changes to the estimate of the target function with changes to the training dataset.
Examples of low-variance machine learning algorithms include Linear Regression, Linear Discriminant Analysis, and Logistic Regression.
Examples of high-variance machine learning algorithms include Decision Trees, k-Nearest Neighbors and Support Vector Machines.
ref:https://medium.com/@mp32445/understanding-bias-variance-tradeoff-ca59a22e2a83#:~:text=Bias%20is%20the%20simplifying%20assumptions%20made%20by%20the%20model%20to,the%20bias%20and%20the%20variance.