Mathematics behind random forest, logistic regression?

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Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions. Logistic regression is a statistical model used to predict the probability of a binary outcome.

Random forest combines multiple decision trees to improve accuracy and reduce overfitting.
Logistic regression is used to model the probability of a binary outcome based on one or more predictor variables.
Random forest is a non-linear model while logistic regression is a linear model.
Random forest is often used for classification tasks while logistic regression is commonly used for binary classification.
Both random forest and logistic regression can handle missing data and categorical variables.

Mathematics behind random forest, logistic regression?

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