Course Content

# Linear Regression with Python

4. Choosing The Best Model

Linear Regression with Python

## What Is The Linear Regression

## Basic concepts

**Regression** is one of the most popular supervised learning tasks.

Its goal is to predict a numerical value (for example, the price of a house) called the **target**, given a set of parameters (size, age, location, etc.), which are called **features**. To train the model, you must provide many examples of such houses, both features and a target. The set of examples you train the model on is called the **training set**.

The simplest model capable of performing regression tasks is a **Linear Regression**.

Let's look at the example of a Simple Linear Regression first.

Consider this scatterplot displaying a person's height and his father's height.

## How it works

What Simple Linear Regression does is just fitting the straight line to the data so that the line is as close to the data points as possible.

## Making the predictions

Now we can use this line to predict the target for a new point.

For example, let's say you want to predict the person's height if his father is 63.5 inches tall. Just pick a point from the line that corresponds to X=63.5, and its y value is our prediction, easy peasy.
The model predicts the person to be 64.3 inches tall.

## Simple Linear Regression Equation

As you may remember from school, the function of a line is **y=b+ax**, so during the training, simple linear regression just learns what values should **a** and **b** have to form a desired line.
The values that the model learns are called **parameters**, and further in a course, we will denote parameters using **𝛽** instead of **a**, **b**.

So our simple linear regression equation is:

In regression, the value we want to predict is called:

Select the correct answer

Everything was clear?