This article is about creating animated plots of simple and multiple logistic regression with batch gradient descent in Python
This article is about creating animated plots of simple and multiple logistic regression with batch gradient descent in Python. In the end, I will also present a visual explanation of why the cross-entropy cost function is the method of choice to quantify costs in logistic regression. In terms of structure and content, this article relates to — and partially builds on — previous articles I wrote about creating animations of batch gradient descent with the example of simple linear and multiple linear regression . The general idea is to set up a logistic regression model and train the model on some arbitrary training data while storing parameter values and costs for each epoch. After confirming our results through sklearn’s built-in logistic regression model, we will use the stored parameter values to generate animated plots with Python’s celluloid module . The animations we use to visualize logistic regression will be similar to the ones we created for previous articles on linear regression.
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