Machine Learning

Linear Discriminant Analysis (LDA) is widely used in machine learning and statistics for pattern classification and dimensionality reduction.

Quadratic Discriminant Analysis (QDA) is a statistical technique used in pattern recognition and machine learning to classify datasets.

Regularized Discriminant Analysis (RDA) is an extension of Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA).

Kernel Discriminant Analysis (KDA), also known as Kernel Fisher Discriminant Analysis, is a nonlinear generalization of Linear Discriminant Analysis.

Logistic Regression is a statistical method for analyzing datasets in which there are one or more independent variables that determine an outcome.

Softmax Regression, commonly known as Multinomial Logistic Regression, is a generalization of logistic regression.

Gaussian Naive Bayes is a variant of Naive Bayes that assumes the likelihood of the features is Gaussian.

Bernoulli Naive Bayes is a variant of the Naive Bayes algorithm, designed specifically for binary/boolean features.

The K-Nearest Neighbors (KNN) classifier is a type of instance-based learning, or lazy learning, where the function is only approximated locally.

The Adaptive KNN Classifier is an extension of the traditional k-NN algorithm, which is widely used for classification and regression.

The Weighted KNN Classifier enhances the conventional KNN algorithm by introducing a weighting scheme for the neighbors based on their distance.

The Linear SVC is a powerful linear model for classification tasks, derived from the Support Vector Machine (SVM) framework.

Kernel Support Vector Classifier (SVC) extends the Support Vector Machine (SVM) concept to handle non-linear data by applying kernel functions.

A Decision Tree Classifier is a non-parametric supervised learning method used for classification and regression tasks.

Affinity Propagation (AP) is a clustering algorithm introduced by Brendan J. Frey and Delbert Dueck in 2007.

Adaptive Affinity Propagation is an extension of the Affinity Propagation algorithm that introduces mechanisms to dynamically adjust the preference.

Kernel Affinity Propagation extends the Affinity Propagation clustering algorithm by incorporating kernel methods to handle non-linear data.

DBSCAN is a popular clustering algorithm that is notable for its ability to find clusters of varying shapes and sizes in a dataset with noise.

The OPTICS algorithm addresses one of DBSCAN's main limitations: the difficulty of identifying clusters of varying density.

DENCLUE is an advanced clustering algorithm that leverages the concept of density functions to identify cluster structures within a dataset.

Mean Shift Clustering is a non-parametric, iterative algorithm that locates the maxima of a density function given discrete data.

Agglomerative Clustering is a type of hierarchical clustering method used to group objects in clusters based on their similarity.

Divisive clustering, also known as DIvisive ANAlysis clustering (DIANA), is a top-down clustering method used in machine learning and data mining.

K-means clustering is a popular unsupervised learning algorithm used to partition n observations into k clusters.

K-means++ enhances the initialization phase of the K-means clustering algorithm, which partitions n observations into k clusters based on their probs.

K-Medians clustering is a partitioning technique that divides a dataset into K groups or clusters by minimizing the median distance between points.

K-Medoids is a clustering algorithm similar to K-Means, with the primary difference being the choice of centroids.

Mini-Batch K-Means is a variant of the K-Means clustering algorithm that aims to reduce the computational cost by using small, random samples.

Fuzzy C-Means (FCM) is an extension of the traditional K-Means clustering algorithm that allows data points to belong to multiple clusters.

Gaussian Mixture Models (GMMs) are a probabilistic model for representing normally distributed subpopulations within an overall population.

Multinomial Mixture Models are probabilistic models used for clustering categorical data.

Spectral Clustering is a versatile algorithm for grouping objects in various applications, such as image and social network segmentation.

Normalized Spectral Clustering refines the standard Spectral Clustering approach by normalizing the data in a way that emphasizes the innate geometry.

Hierarchical Spectral Clustering combines the principles of hierarchical clustering with spectral methods to analyze data structures at various scale.

Adaptive Spectral Clustering enhances the spectral clustering framework by dynamically adjusting the algorithm's parameters.

The Bagging Classifier is a machine learning ensemble meta-algorithm designed to improve the stability an accuracy of ML algorithms.

The Bagging Regressor is an ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms.

AdaBoost is an ensemble learning method that is used primarily for classification tasks. It combines multiple weak classifiers to create a strong one.

AdaBoost Regressor is an ensemble learning method specifically adapted for regression problems. It combines multiple weak regressors.

Gradient Boosting Classifier is a powerful machine learning algorithm that belongs to the ensemble methods, specifically to boosting techniques.

Gradient Boosting Regressor is a potent machine learning algorithm that is part of the ensemble methods, particularly within the boosting techniques.

Random Forest Classifier is a powerful machine learning algorithm used for classification tasks. It operates by constructing a multitude of trees.

Random Forest Regressor is a machine learning algorithm used for regression, which predicts a continuous value for new observations by aggregation.

The Voting Classifier is a machine learning model that combines the predictions from multiple different models to make a final prediction.

Voting Regressor is an ensemble machine learning algorithm used for regression tasks. It combines the predictions from several different regressors.

Stacking involves training a model to combine the predictions of several other models. The aim is to use the stacked model to achieve better score/

Stacking Regressor is an ensemble learning technique that combines multiple regression models via a meta-regressor.

Accuracy is one of the most intuitive and widely used performance metrics for evaluating classification models in machine learning.

Precision is a metric in the evaluation of classification models within machine learning, particularly in scenarios where the cost of FP is high.

Recall is a vital metric for evaluating the performance of classification models, especially in contexts where FN carries significant consequences.

The F1 Score is a widely used metric for measuring a model's accuracy on datasets where true negatives don't matter as much.

Specificity(TNR) is a performance metric used to evaluate the effectiveness of a classification model in identifying negative instances correctly.

The Silhouette Coefficient is a metric used to calculate the effectiveness of clustering algorithms.

The Davies-Bouldin Index (DBI) is a metric for evaluating clustering algorithms. The lower the DBI value, the better the clustering quality.

Inertia, often referred to in the context of k-means clustering, is a metric used to evaluate the quality of cluster assignments.

Mean Absolute Error (MAE) is a metric used to evaluate the performance of regression models. It quantifies the average magnitude of errors.

Mean Squared Error is a statistical measure used to evaluate the performance of regression models. It calculates the average of the squares of errors.

Root Mean Squared Error (RMSE) is a standard way to measure the error of a model in predicting quantitative data.

Mean Absolute Percentage Error (MAPE) is a statistical measure often used to assess the accuracy of forecast models.

R-Squared, also known as the coefficient of determination, is a statistical measure used to assess the goodness of fit of a regression model.

Adjusted (Adjusted Coefficient of Determination) enhances the traditional R-Squared metric by adjusting for the number of predictors in a regression.

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into PCs.

Linear Discriminant Analysis (LDA) is a supervised machine learning algorithm used for both classification and dimensionality reduction.

KPCA is an extension of PCA that utilizes kernel methods to perform nonlinear dimensionality reduction.

Truncated SVD is a matrix factorization technique that reduces the dimensionality of data by truncating the SVD of a matrix.

Factor Analysis is a statistical method used to describe variability among observed, correlated variables in terms of a lower number of 'factors'.

Kernel Discriminant Analysis is an extension of Linear Discriminant Analysis that employs kernel methods to find a linear combination of features.

CCA is a multivariate statistical method concerned with understanding the relationships between two sets of variables.

t-SNE is a powerful machine learning algorithm for dimensionality reduction, particularly for the visualization of high-dimensional datasets.

Multidimensional Scaling (MDS) is a statistical technique used for analyzing similarity or dissimilarity data.

Metric MDS is a form of MDS that focuses on preserving the metric distances between point in a high-space when mapping them to a low-space.

Landmark MDS is an advanced variation of the classical MDS technique, which is aimed at dimensionality reduction and visualization.

Locally Linear Embedding (LLE) is a non-linear dimensionality reduction technique widely used for exploring the structure of high-dimensional data.

MLLE is an enhanced version of the classic LLE algorithm, designed to address some of its limitations, particularly in preserving the local geometry.

HLLE is an advanced non-linear dimensionality reduction technique used to unfold high-dimensional data into lower-dimensional spaces.

Sammon Mapping is a non-linear dimensionality reduction technique introduced by John W. Sammon in 1969.

Laplacian Eigenmap is a dimensionality reduction technique used in machine learning and data science to project high-dimensional data into a lower-dim

C-Isomap refines the Isometric Mapping (Isomap) technique by incorporating the principle of conformality into the dimensionality reduction.

LTSA is a prominent technique in the realm of non-linear dimensionality reduction, focusing on preserving the local geometry of high-dimensional data.

SBS is a feature selection technique used in machine learning to reduce the dimensionality of the data by sequentially removing features.

Sequential Forward Selection (SFS) is a heuristic algorithm used in machine learning for feature selection.

RFE is a feature selection method used in machine learning to identify and select features by recursively considering smaller sets of features.

Poisson Regression is a statistical approach used to model count data, particularly for outcomes that represent counts or rates following Pois. dist.

Negative Binomial Regression offers a robust alternative to Poisson regression for modeling count data, particularly when overdispersion occurs.

Gamma Regression is utilized for modeling positive continuous data with skewed distributions.

Beta Regression is tailored for modeling variables that take values in the open interval (0, 1), making it ideal for proportions.

Inverse Gaussian Regression is a specialized regression model used for positive continuous outcomes, particularly when the data exhibit a long tail.

Ridge regression, also known as Tikhonov regularization, is a technique used for analyzing multiple regression data that suffer from multicollinearity

Lasso regression, standing for Least Absolute Shrinkage and Selection Operator, is a regression analysis method that performs variable selection & reg

Elastic Net Regression is an advanced regularization technique that synergizes the regularization aspects of both Lasso and Ridge regularization.

Linear regression is a foundational statistical method used to model the relationship between a dependent variable and one or more indep. variables.

Kernel Ridge Regression is an advanced machine learning algorithm that combines ridge regression's regularization techniques with the kernel trick.

Bayesian Ridge Regression extends traditional ridge regression by incorporating Bayesian inference, offering a probabilistic approach to regression.

The k-Nearest Neighbors regressor is a type of instance-based learning, or lazy learning, where the function is only approximated locally.

Adaptive k-Nearest Neighbors (Adaptive KNN) regression is an enhanced variant of the standard KNN regression algorithm.

Weighted k-Nearest Neighbors regression is an advanced variation of the basic KNN regression algorithm, which assigns weights to their contributions.

Polynomial regression is a statistical technique that expands the capabilities of linear regression by modeling the relationship between the variables

RANSAC is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers.

MLESAC algorithm extends the RANSAC methodology by incorporating a probabilistic framework for model fitting in data sets with significant outlier.

Support Vector Regression (SVR) extends the principles of Support Vector Machines (SVMs) from classification to regression problems.

Kernel SVR is an advanced machine learning algorithm that extends the concept of Support Vector Regression by employing kernel functions.

Decision Tree Regression is a versatile machine learning algorithm used for predicting a continuous quantity.

The Local Outlier Factor (LOF) algorithm is a density-based outlier detection method used in data analysis and machine learning to identify anomalies.