Linear Regression

 

One of the simplest Machine learning algorithms out there, Linear Regression is used to make predictions on continuous dependent variables with knowledge from independent variables. A dependent variable is the effect, in which its value depends on changes in the independent variable.

You may remember the line of best fit from school – this is what Linear Regression produces. A simple example is predicting one’s weight depending on their height.

 

Logistic Regression

 

Logistic Regression, similar to Linear Regression, is used to make predictions on categorical dependent variables with knowledge of independent variables. A categorical variable has two or more categories. Logistic Regression classifies outputs that can only be between 0 and 1.

For example, you can use Logistic Regression to determine whether a student will be admitted or not to a particular college depending on their grades – either Yes or No, or 0 or 1.

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