# Glossary of Classification Models

Here is a glossary of some common terms used in classification machine learning:

Classification: A type of machine learning problem where the goal is to predict a discrete class or category for a given input.

Binary Classification: A type of classification where the goal is to predict one of two possible classes or outcomes.

Multiclass Classification: A type of classification where the goal is to predict one of three or more possible classes or outcomes.

Decision Boundary: The boundary that separates different classes in a classification problem.

Feature: A measurable aspect or characteristic of the input data that is used to make predictions in a machine learning model.

Feature Extraction: The process of identifying and selecting relevant features from raw data.

Label: The output or target variable in a classification problem that represents the class or category being predicted.

Ensemble Learning: A technique that involves combining multiple machine learning models to improve the accuracy and robustness of predictions.

Naive Bayes: A probabilistic algorithm that uses Bayes' theorem to predict the probability of a class given a set of features.

K-Nearest Neighbors (KNN): A non-parametric method used for classification that predicts the class of a new observation based on the classes of its nearest neighbors in the feature space.

Decision Tree: A non-parametric model that uses a tree structure to represent decisions and their possible consequences. It recursively splits the data into smaller and smaller subsets based on the values of the features, until the subsets are homogeneous with respect to the target variable. Random Forest: An ensemble learning algorithm that constructs multiple decision trees and combines their predictions to improve the accuracy and robustness of predictions.

Support Vector Machine (SVM): A linear model used for binary classification that seeks to find the decision boundary that maximizes the distance between the boundary and the closest data points.

Neural Networks: A type of machine learning model that is inspired by the structure and function of the human brain. Neural networks can be used for both binary and multiclass classification problems.

Gradient Boosting: An ensemble learning algorithm that uses a combination of weak learners, such as decision trees, to make accurate and robust predictions.

These are just a few of the terms and techniques used in classification machine learning. As with any field, there are many more specialized terms and techniques that can be learned with further study and practice.

Classification: A type of machine learning problem where the goal is to predict a discrete class or category for a given input.

Binary Classification: A type of classification where the goal is to predict one of two possible classes or outcomes.

Multiclass Classification: A type of classification where the goal is to predict one of three or more possible classes or outcomes.

Decision Boundary: The boundary that separates different classes in a classification problem.

Feature: A measurable aspect or characteristic of the input data that is used to make predictions in a machine learning model.

Feature Extraction: The process of identifying and selecting relevant features from raw data.

Label: The output or target variable in a classification problem that represents the class or category being predicted.

Ensemble Learning: A technique that involves combining multiple machine learning models to improve the accuracy and robustness of predictions.

Naive Bayes: A probabilistic algorithm that uses Bayes' theorem to predict the probability of a class given a set of features.

K-Nearest Neighbors (KNN): A non-parametric method used for classification that predicts the class of a new observation based on the classes of its nearest neighbors in the feature space.

Decision Tree: A non-parametric model that uses a tree structure to represent decisions and their possible consequences. It recursively splits the data into smaller and smaller subsets based on the values of the features, until the subsets are homogeneous with respect to the target variable. Random Forest: An ensemble learning algorithm that constructs multiple decision trees and combines their predictions to improve the accuracy and robustness of predictions.

Support Vector Machine (SVM): A linear model used for binary classification that seeks to find the decision boundary that maximizes the distance between the boundary and the closest data points.

Neural Networks: A type of machine learning model that is inspired by the structure and function of the human brain. Neural networks can be used for both binary and multiclass classification problems.

Gradient Boosting: An ensemble learning algorithm that uses a combination of weak learners, such as decision trees, to make accurate and robust predictions.

These are just a few of the terms and techniques used in classification machine learning. As with any field, there are many more specialized terms and techniques that can be learned with further study and practice.

Updated on: 21/02/2023

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