ML Basics on English AI Terms Dictionary

Regularization

Sat, 18 Jul 2026 10:13:50 +0000

Definition

Regularization is a crucial concept in machine learning designed to reduce generalization error without significantly increasing training error. It works by discouraging models from learning overly complex patterns that fit noise in the training data rather than the underlying signal. Common methods include L1 (Lasso) and L2 (Ridge) regularization, dropout in neural networks, and early stopping. These techniques help ensure that the model performs well on unseen data by maintaining a balance between bias and variance.

Normalization

Sat, 18 Jul 2026 10:09:07 +0000

Definition

Common methods include Min-Max scaling and Z-score standardization. This process ensures that features with larger magnitudes do not dominate the learning algorithm, particularly in gradient-based optimization like neural networks. By normalizing input data, models train faster and achieve better stability. It is a critical step in preparing datasets for machine learning pipelines to ensure equitable contribution from all variables.

Summary

Normalization is a data preprocessing technique that scales numerical features to a standard range, typically between 0 and 1, to improve model convergence and performance.

Generative model

Sat, 18 Jul 2026 09:59:34 +0000

Definition

Generative models are algorithms designed to understand the patterns and structures within a given dataset so they can create new data instances that resemble the original. Unlike discriminative models that classify data, generative models learn the joint probability distribution P(X,Y). Common architectures include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. They are fundamental to modern AI applications involving image synthesis, text generation, and audio creation.

Summary

A type of machine learning model that learns the underlying distribution of data to generate new, synthetic samples similar to the training data.

Feature

Sat, 18 Jul 2026 09:57:52 +0000

Definition

In machine learning, a feature is a distinct attribute or variable that describes an instance within a dataset. Features can be numerical, categorical, or textual, and they serve as the fundamental inputs for training predictive models. The quality and relevance of features directly impact model performance, as they determine how well the algorithm can learn patterns and make accurate predictions on new, unseen data.

Summary

An individual measurable property or characteristic of a phenomenon being observed, serving as input data for machine learning models.

Binary classification

Sat, 18 Jul 2026 09:48:19 +0000

Definition

Binary classification is a fundamental machine learning problem where the output variable is categorical with exactly two possible outcomes, such as true/false or spam/not spam. Algorithms like logistic regression, support vector machines, and decision trees are commonly used. The model learns a decision boundary that separates the two classes based on training data features, enabling predictions for new, unseen instances.

Summary

A supervised learning task where the goal is to predict one of two possible classes for each input instance.

Supervised Learning

Sat, 18 Jul 2026 09:42:48 +0000

Definition

In supervised learning, the algorithm is trained on a labeled dataset, meaning each input example is paired with the correct output. The goal is for the model to learn the underlying relationship between inputs and outputs so it can accurately predict labels for unseen data. Common tasks include classification, where discrete categories are predicted, and regression, where continuous values are estimated.

Summary

A machine learning paradigm where a model learns to map inputs to outputs based on labeled training examples.