Classification

AUC Score measures the performance of a binary classification model at various threshold settings.

Bernoulli Naive Bayes is a probabilistic classifier based on Bayes' theorem, suitable for binary features.

Class weighting adjusts the importance of different classes in machine learning to address imbalanced datasets.

Classification and Regression Trees (CART) are decision tree algorithms used for predicting outcomes based on input features.

Coarse-grained classification involves categorizing data into broad, high-level groups rather than fine, specific categories.

A decision boundary is a surface that separates different classes in a dataset used for classification tasks.

A decision stump is a simple machine learning model that uses one feature to make a binary classification decision.

A Decision Tree Classifier is a machine learning model used for classification tasks, utilizing a tree-like structure to make decisions.

A false positive in AI refers to an incorrect result where a model incorrectly identifies a positive outcome.

Functional Gradient Boosting is a machine learning technique that builds models in a stage-wise manner to improve prediction accuracy.

Imbalanced classes occur when one class in a dataset significantly outnumbers others, affecting model training and performance.

K-Nearest Neighbors (KNN) is a simple algorithm used for classification and regression based on the closest training examples.

Kernelized SVM is an advanced machine learning technique that classifies data by transforming it into higher dimensions.

A method that enhances nearest neighbor classification by maximizing the margin between different classes.

A Linear Support Vector Machine classifies data by finding the optimal hyperplane that separates different classes in a dataset.

Linearly separable refers to datasets where classes can be separated by a straight line (or hyperplane) in their feature space.

A Logistic Classifier is a statistical model used for binary classification tasks, predicting probabilities of outcomes.

Logit is a function used to model binary outcomes in statistics and machine learning.

The majority class refers to the category in a dataset that has the highest frequency of instances.

A margin classifier is a type of machine learning algorithm that separates data points using a hyperplane while maximizing the margin between classes.

A max-margin classifier is a type of machine learning model that finds the hyperplane maximizing the margin between classes.

The minority class refers to the less frequently occurring category in a classification problem, often leading to data imbalance issues.

Misclassification error measures the rate at which a model incorrectly predicts the class of data points.

Multi-Class Classification is a supervised learning task that categorizes inputs into multiple classes or categories.

A Nearest Centroid Classifier identifies class labels based on the proximity to the centroid of each class in feature space.

The negative class refers to the category of data points that do not possess the target attribute in classification tasks.

A Neutral Class in AI refers to a category representing data that does not belong to any specific labeled class.

One-Class Classification identifies instances of a single class, distinguishing them from all other potential data points.