Bias is, assumptions made by a model to make the target function easier to learn; therefore :

: refers to less assumptions made about the form of the target function.*LOW BIAS*: refers to high assumptions made about the form of the target function.*HIGH BIAS*

Low-bias machine learning algorithms include: *Decision Trees, k-Nearest Neighbors and SVMs (Support Vector Machines)*.

High-bias machine learning algorithms include: *Linear Regression, Linear Discriminant Analysis and Logistic Regression.*

Ont the other hand, Variance is the estimate value of change of Target function if different training data was used, therefore:

**Low Variance**: Suggests small changes to the estimate of the target function with changes to the training dataset.**High Variance**: Suggests large changes to the estimate of the target function with changes to the training dataset.

Low-variance machine learning algorithms include: *Linear Regression, Linear Discriminant Analysis and Logistic Regression.
*High-variance machine learning algorithms include:

*Decision Trees, k-Nearest Neighbors and Support Vector Machines.*

**Overfitting**

**Overfitting**occurs when a statistical model or machine learning algorithm**captures the noise**of the data.- Intuitively, overfitting occurs when the model or the algorithm fits the data too well.
- Specifically, overfitting occurs if the model or algorithm shows
**low bias**but**high variance**. - Overfitting is often a result of an excessively complicated model,

**Underfitting**

**Underfitting**occurs when a statistical model or machine learning algorithm**cannot capture the underlying trend**of the data.- underfitting occurs when the model or the algorithm does not fit the data well enough.
- underfitting occurs if the model or algorithm shows
**low variance**but**high bias**. - Underfitting is often a result of an excessively simple model.