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    Tables – Mathematics – Statistical Algorithms

    Statistical AlgorithmDescription
    Linear RegressionA basic algorithm predicting quantitative outcomes based on independent variables.
    Logistic RegressionIt’s used for binary classification problems, predicting a probability between 0 and 1.
    K-Nearest Neighbours (KNN)A multi-use algorithm that can perform both classification and regression.
    Support Vector Machine (SVM)An algorithm used typically for binary classification problems.
    Decision TreesAn algorithm used for solving both regression and classification problems.
    Random ForestsIt uses ensemble learning method for classification, regression and other tasks.
    Naive BayesBased on Bayes’ Theorem, this algorithm is particularly suited when the dimensionality of the inputs is high.
    K-means ClusteringAn iterative algorithm that divides a group of n datasets into k subgroups/clusters.
    Gradient Boosting Algorithms (GBM)A machine learning technique for regression and classification problems.
    Principal Component Analysis (PCA)A technique used to emphasize variation and bring out strong patterns in a dataset.
    Artificial Neural Networks (ANN)A computing system inspired by the biological neural networks that constitute animal brains.
    Deep LearningA class of machine learning algorithms that use artificial neural networks with multiple layers.
    Time Series AlgorithmsA family of algorithms designed specifically for use in time series data.
    Hierarchical ClusteringA method of clustering where you build nested clusters by merging or splitting them successively.
    Association Rule LearningA rule-based machine learning method for discovering relationships between variables in large databases.
    Ridge RegressionA technique for analyzing multiple regression data that suffer from multicollinearity.
    Lasso RegressionA regression analysis method that performs both variable selection and regularization.
    Elastic NetA regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.
    Factor AnalysisA statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables.
    Discriminant AnalysisIt is used when the dependent variable is categorical and the independent variable is interval in nature.