Tools: Before You Touch A Neural Network, Master These 3 Classifiers

Posted on Jan 19

• Originally published at Medium

Open LinkedIn and you’ll see buzz everywhere — Transformers, LLMs, and Generative AI. It’s easy to feel left behind if you’re not fine-tuning massive models. But here’s the truth: complex problems don’t always need complex solutions. (Medium)

Complex problems don’t always need complex solutions. Think of it this way: you don’t use a flamethrower to light a candle. Before you try to master the “magic” of Deep Learning, you need to master the reliability of the classics. (Medium)

In this post, we’ll break down three essential supervised learning algorithms that form the foundation of machine learning. They are fast, effective, and — unlike deep neural networks — interpretable. (Medium)

Imagine you move into a new neighborhood but don’t know if it’s a “party” or “quiet” area. So you look at your three closest neighbors:

Since most neighbors are partying, you assume you’re in a party neighborhood. That’s the essence of K-Nearest Neighbors — you classify a new point based on the “vote” of the K closest labeled points. (Medium)

Using scikit-learn makes this model quick and simple to train and use. (Medium)

KNN tells you what class something is — but sometimes you want how confident the model is. Logistic Regression works like a dimmer switch rather than a binary light switch. (Medium)

Instead of a straight yes/no boundary, it fits an S-shaped curve (Sigmoid) to your data. Predictions are probabilities:

This nuance is crucial in business contexts — e.g., deciding whether to send an email or make a call based on churn probability. (Medium)

Source: Dev.to