```
tags:
- AI/ML/SupervisedLearning
```

Regression Models investigate and discover the relationship between dependent(target,

Linear Assumption

Regression Models assume a linear dependency between the dependend and independent variables. If such a Linear Relationship doens't exist, then regression is not a good solution and machine learning model should be used that don't follow Linear Assumption.

Attributes of regression models:

- Number of independent variables
- Univariate: 1 feature or independent variables
- Bivariate: 2 features or independent variables
- Multivariate: more than 2 feature or independent variables

- Shape of the regression
- Type of dependent variables

Algorithms:

Notes:

- Regression models are based on mathematical Regression Mathematics topic.
- Random Forest can be utilized in regression tasks. Then the mean or average prediction of the individual trees is returned.
- Regression models are highly interpretable and fast to train.
- Based on degree of independent variable they are divided into:
- Linear Regression: Where it is a straight line that fits into the data points.
- Polynomial Regression: Where it is a curve that fits into the data points.

- MSE and MAE Loss Functions are used for regression tasks.
- Regression models assume Linearity, Multivariate normality, Homoscedasticity, and no Multicollinearity.

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