Building a Simple Logistic Regression from Scratch in Python
This article provides a step-by-step guide to building a simple logistic regression model in pure Python, without using any machine learning libraries. It covers training a one-feature model, scaling to two features, and using gradient descent to optimize the model.
Why it matters
This tutorial provides a transparent, step-by-step implementation of logistic regression from scratch, which can help readers better understand the inner workings of this fundamental machine learning algorithm.
Key Points
- 1Implement logistic regression from scratch using NumPy
- 2Train a one-feature model and a two-feature model
- 3Use gradient descent to iteratively minimize the cross-entropy loss
- 4Predict the probability of a new sample belonging to the positive class
Details
The article starts by setting up toy data for a binary classification problem, with feature values X and binary labels y. It then defines the logistic regression function, which includes the linear part (calculating z = mx + b) and the sigmoid activation function to get the predicted probability y_hat. The gradients for the weight (m) and bias (b) parameters are calculated using the cross-entropy loss, and these parameters are updated using gradient descent. The process is repeated for a specified number of epochs. The article also demonstrates how to extend the one-feature model to a two-feature model. Finally, it shows how to use the trained model to predict the probability for a new single-feature or two-feature sample.
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