A kind of simple probabilistic classification model based on Bayes’ theorem with the assumption of independence between features.

SVM aims to create a decision boundary called maximum margin separating hyperplane that enables the prediction of labels from one or more features.

An ensemble estimator that fits a number of decision tree classifiers on various sub-samples of the dataset

Logistic regression adds a nonlinear function to a linear function to estimate the probability of an event occurring.

A non-parametric method which assigns the samples with labels by the majority rule on the labels of selected nearest neighbors.

A scalable and highly accurate implementation of gradient boosting which integrates multiple parallel tree models to build a more powerful learner model.

A gradient boosting framework that uses tree based learning algorithms.it uses histogram-based algorithms to speed up training and reduce memory usage.

An ensemble estimator based on multiple weak prediction models.It can adjust the weight of misclassified instances so that subsequent classifiers focus more on difficult cases.

It establishes a linear relationship between label and features to minimize the residual sum of squares between the observed targets and the predicted targets.

It aims to create a decision boundary called maximum margin separating hyperplane such that all data in a set are at the shortest distance from the hyperplane

A linear regression model where the loss function is the linear least squares function and regularization is given by the l2-norm.

a linear regression model that minimizes the residual sum of squares so it tends to produce some coefficients that are exactly 0 and hence gives interpretable models.

A non-parametric model that predicts the values of target variables by learning simple decision rules inferred from data features.

A scalable and highly accurate implementation of gradient boosting which integrates multiple parallel tree models to build a more powerful learner model.

An ensemble estimator that fits a number of decision tree classifiers on various sub-samples of the dataset

An ensemble estimator based on multiple weak prediction models.It can adjust the weight of misclassified instances so that subsequent classifiers focus more on difficult cases.

Support vector machine (SVM) method is used for survival analysis.

SurvivalTree method is used for survival analysis.

An ensemble estimator that fits a number of randomized survival trees on various sub-samples of the dataset.

An ensemble estimator that fits a number of survivaltrees on various sub-samples of the dataset.