Frequentist vs. Bayesian Approaches in Machine Learning

Frequentist vs. Bayesian Approaches in Machine Learning

A Comparison of Linear Regression and Bayesian Linear Regression. In this article, we will talk about their differences and connections in the context of machine learning. We will also use two algorithms for illustration: linear regression and Bayesian linear regression.

There has always been a debate between Bayesian and frequentist statistical inference. Frequentists dominated statistical practice during the 20th century. Many common machine learning algorithms like linear regression and logistic regression use frequentist methods to perform statistical inference. While Bayesians dominated statistical practice before the 20th century, in recent years many algorithms in the Bayesian schools like Expectation-Maximization, Bayesian Neural Networks and Markov Chain Monte Carlo have gained popularity in machine learning.

In this article, we will talk about their differences and connections in the context of machine learning. We will also use two algorithms for illustration: linear regression and Bayesian linear regression.

bayesian-machine-learning linear-regression frequentists machine-learning bayesian-statistics

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Linear Regression VS Logistic Regression (MACHINE LEARNING)

Linear Regression VS Logistic Regression (MACHINE LEARNING). Linear Regression and Logistic Regression are two algorithms of machine learning and these are mostly used in the data science field.

Applications of machine learning in different industry domains

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Regression: Linear Regression

Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics.