Supervised Learning: When to Use Linear vs. Logistic Regression

Linear vs. Logistic Regression: Learn key differences, use cases, and Python examples to master supervised learning and build smarter predictive models.

We've now reached a big moment in our machine learning journey: supervised learning.

So far in our Machine Learning series, we’ve covered collecting data, cleaning it up, engineering features, and doing some exploratory analysis. These steps matter because a machine learning model is only as good as the data it learns from.

Now, we’re diving into predictive modeling for the first time! Yay 😁

Supervised learning is a key type of machine learning where an algorithm learns from labeled data. If I boil it all down, this means we already know the right answer for each item in a dataset.

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The model’s job is to recognize patterns and make accurate predictions. This approach is used everywhere—healthcare, finance, marketing, cybersecurity—you name it.

There are two main types of supervised learning: regression and classification. In this article, I’ll break down both for you guys so you can get comfortable with them.

First, we’ll look at linear regression, a simple but powerful tool for making predictions. Then, we’ll move on to classification using logistic regression.

By the end, you’ll know have gained deeper insights in Supervised Learning and how we can get building models today with Scikit-Learn.

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Alright, let’s get into some supervised learning nerds, enjoy this weeks article!