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Linear Regression

Source: Wikipedia â€™Linear Regressionâ€™

Linear regression is an algorithm (belonging to both statistics and machine learning) that models the relationship between two or more variables by fitting a linear equation to a dataset. Independent variables are the features (input data) and dependent variables are the target (what you are trying to predict).

The technique is very simple and can be represented by this familiar equation:

However, this is typically written slightly differently in machine learning:

Or for a more advanced model with multiple features:

Where:

*y*is the predicted label*b*is the bias (the intercept)*w1*is the coefficient or weight of the first feature (weight =*m*or slope)*x1*is a feature (an input)

Like logistic regression, gradient descent is typically used to optimize the values of the coefficients (each input value or column) by iteratively minimizing the loss of the model during training.

Linear vs Logistic Regression

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Linear vs Logistic Regression