Types of Self Learning

Dependent and independent variables

As with other fields of statistical inquiry, machine learning is based on the cross-analysis of dependent and independent variables. The dependent variable (y) is the output you wish to predict and the independent variable (x) is an input that supposedly impacts the dependent variable (output).

Machine learning aims to find how the independent variable/s (x) affects the dependent variable (y).

For example: to predict the value of a house, a machine learning framework called “supervised learning” analyzes the relationship between house features (distance to the city, number of rooms, land size, etc.) as independent variables and the selling price of other houses in the neighborhood as the dependent variable to design a prediction model. The prediction model can then predict the value (y) of a house with an unknown selling price by inputting its features (x) into the prediction model.