05-linear-regression-simple.md

2.5 Linear regression

Slides

Notes

Model for solving regression tasks, in which the objective is to adjust a line for the data and make predictions on new values. The input of this model is the feature matrix X and a y vector of predictions is obtained, trying to be as close as possible to the actual y values. The linear regression formula is the sum of the bias term ( $w_0$ ), which refers to the predictions if there is no information, and each of the feature values times their corresponding weights as ( $x_{i1}\cdot w_1+x_{i2}\cdot w_2+...+x_{in}\cdot w_n$ ).

So the simple linear regression formula looks like:

$g(x_i)=w_0+x_{i1}\cdot w_1+x_{i2}\cdot w_2+...+x_{in}\cdot w_n$.

And that can be further simplified as:

$g(x_i)=w_0+{\displaystyle \sum _{j=1}^n{w}_j\cdot x_{ij}}$

Here is a simple implementation of Linear Regression in python:

w0 = 7.1
def linear_regression(xi):
    
    n = len(xi)
    
    pred = w0
    w = [0.01, 0.04, 0.002]
    for j in range(n):
        pred = pred + w[j] * xi[j]
    return pred

If we look at the ${\displaystyle \sum _{j=1}^n{w}_j\cdot x_{ij}}$ part in the above equation, we know that this is nothing else but a vector-vector multiplication. Hence, we can rewrite the equation as $g(x_i)=w_0+x_i^T\cdot w$

We need to assure that the result is shown on the untransformed scale by using the inverse function exp().

The entire code of this project is available in this jupyter notebook.

⚠️	The notes are written by the community. If you see an error here, please create a PR with a fix.

• Notes from Peter Ernicke

Navigation

• Machine Learning Zoomcamp course
• Session 2: Machine Learning for Regression
• Previous: Setting up the validation framework
• Next: Linear regression: vector form