Machine Learning: Python Linear Regression Estimator Using Gradient Descent

Author(s): Benjamin Obi Tayo Ph.D.

Originally published on Towards AI.

Implementation Using Python Estimator

In this article, we describe how a simple python estimator can be built to perform linear regression using the gradient descent method. Let’s assume we have a one-dimensional dataset containing a single feature (X) and an outcome (y), and let’s assume there are N observations in the dataset:

A linear model to fit the data is given as:

where w0 and w1 are the weights that the algorithm learns during training.

If we assume that the error in the model is independent and normally distributed, then the likelihood function is given as:

To maximize the likelihood function, we minimize the sum of squared errors… Read the full blog for free on Medium.

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Published via Towards AI