INTRODUTION TO REGRESSION IN MACHINE LEARNING:

The following article provides an outline for Regression in Machine Learning. Regression means to predict the value using the input data. Regression models are used to predict a continuous value. It is mostly used to find the relationship between the variables and forecasting. Regression models differ based on the kind of relationship between dependent and independent variables.

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Types of Regression in Machine Learning:

There are different types of regression:

1. Simple Linear Regression: Simple linear regression is a target variable based on the independent variables. Linear regression is a machine learning algorithm based on supervised learning which performs the regression task.
2. Polynomial Regression: Polynomial regression transforms the original features into polynomial features of a given degree or variable and then apply linear regression to it.
3. Support Vector Regression: Support vector regression identifies a hyperplane with the maximum margin such that the maximum number of data points is within the margin.
4. Decision Tree Regression: The decision tree is a tree that is built by partitioning the data into subsets containing instances with similar values. It can use for regression and classification also.
5. Random Forest Regression: Random forest is an ensemble approach where we take into account the predictions of several decision regression trees.

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Implementation of Linear Regression in Machine Learning

Linear regression is employed in varied ways in which a number of them are listed as:

• Sales prognostication
• Risk analysis
• Housing applications
• Finance applications

The process used for implementing the statistical regression whereas exploitation it in many ways in which some are mentioned below:

• Loading the data
• Exploring the data
• Slicing the data
• Train and split data
• Generate the model
• Evacuate the accuracy

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Advantages and Disadvantages of Linear Regression

1. Advantages:

• Linear regression performs well when the data set is linearly separable. We can use it to find the nature of the relationship between the variables.
• It is easier to implement, interpret and very efficient to train.
• It is prone to over-fitting but it can be easily avoided using some dimensionality reduction techniques, regularization techniques, and cross-validation.
• It has the extrapolation beyond the specific data set.

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2. Disadvantages:

• Linear assumption: It assumes that the relationship between the input and the output is linear.
• Remove noise: It assumes that the input and the output variables are not noisy.
• Remove collinearity: It will over-fit the data when we have highly correlated input variables.
• Gaussian distributions: It will create a lot of reliable predictions if the input and output variables have a Gaussian distribution.
• Resize inputs: It usually creates a lot of reliable predictions if we tend to use resize input variables exploitation standardization or social control.
• Susceptible to outliers: It is very sensitive to outliers. So, the outliers need to be removed before applying the linear regression to the data set.

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