machine_learning.ordinary_least_squares_regression

Ordinary Least Squares Regression (OLSR):

Ordinary Least Squares Regression (OLSR) is a statistical method for estimating the parameters of a linear regression model. It is the most commonly used regression method, and it is based on the principle of minimizing the sum of the squared residuals.

Below is a simple implementation of OLSR without using any external libraries.

WIKI: https://en.wikipedia.org/wiki/Ordinary_least_squares

Attributes

x_points

Functions

ols_regression(→ tuple)

Performs Ordinary Least Squares Regression (OLSR) on the given data.

Module Contents

machine_learning.ordinary_least_squares_regression.ols_regression(x_point: numpy.ndarray, y_point: numpy.ndarray) → tuple

Performs Ordinary Least Squares Regression (OLSR) on the given data.

Args:

x: The independent variable. y: The dependent variable.

Returns:

a (float): The intercept of the regression line. b (float): The slope of the regression line.

Examples: >>> x = np.array([1, 2, 3, 4, 5]) >>> y = np.array([2, 4, 6, 8, 10]) >>> a, b = ols_regression(x, y) >>> float(a) # Intercept should be 0.0 0.0 >>> float(round(b, 2)) # Slope should be 2.0 2.0

machine_learning.ordinary_least_squares_regression.x_points