💡 Linear Models

Linear Models#

Models predicting from a weighted sum of features.

Important

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What it is#

A linear model predicts from a weighted sum of the input features, optionally passed through a link function:

\[\hat{y} = \mathbf{w}^\top \mathbf{x} + b.\]

Its defining trait is that it is linear in the parameters, which makes it simple, fast, and highly interpretable.

Both tasks#

The family spans regression (linear regression, ridge, lasso) and classification (logistic regression, linear SVM), where the linear combination is squashed by a sigmoid or softmax into probabilities. In every case the learned weights show each feature’s direction and strength.

Strengths and limits#

Linear models are data-efficient, cheap to train and serve, and transparent — but they can only capture linear relationships unless you add interactions or feature transforms. They are the natural baseline against which more complex models must justify themselves.

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Mind map — connected ideas

Logistic Regression · Classification Models · Regression Models · Neural Networks · Loss Functions · Support Vector Machines (SVMs)

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AI (Artificial Intelligence) · Classification Models · Computer Vision (CV) · Decision Trees · 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