💡 Classification Models

Classification Models#

Models that assign inputs to discrete categories.

Important

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What it is#

Classification models predict a discrete category — spam or not, which digit, which disease. The output is a class label (often via a probability over classes), and the model learns a decision boundary that separates the classes in feature space.

The landscape#

They range from linear ones (logistic regression, linear SVM) to non-linear ones (decision trees, random forests, neural networks, kernel SVMs). Tasks split into binary (two classes), multiclass (one of many), and multilabel (several at once).

How they’re judged#

Because the target is categorical, classification uses metrics like accuracy, precision / recall, F1, and AUC — not squared error — and its loss functions are typically cross-entropy rather than a distance. The right metric depends on class balance and error costs.

Theme: AI & ML Concepts · All terminology

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Mind map — connected ideas

Regression Models · Linear Models · Logistic Regression · Decision Trees · Binary Classification · Multiclass Classification

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More in AI & ML Concepts

AI (Artificial Intelligence) · Computer Vision (CV) · Decision Trees · Linear Models · LLMs (Large Language Models) · Logistic Regression · Machine Learning (ML) · Medical AI · Natural Language Processing (NLP) · Neural Networks · Regression Models · Support Vector Machines (SVMs) · Target Variable