Maximum likelihood estimation

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Maximum Likelihood Estimation (MLE) is a fundamental concept in statistics and machine learning used for estimating the parameters of a statistical model. The core idea behind MLE is to find the parameter values that maximize the likelihood function, which measures how well the model explains the observed data.

To perform MLE, you start by defining the likelihood function based on the chosen statistical distribution. For example, if you are working with a Poisson distribution, the likelihood function will depend on the parameter λ, which represents the average number of occurrences in a given interval. Once you have the likelihood function, you can take its logarithm to simplify the calculations, leading to the log-likelihood function. The next step involves differentiating this log-likelihood function with respect to the parameter and setting the result equal to zero to find the maximum ³⁴.

In practice, MLE is widely used in various applications, including logistic regression and other statistical modeling techniques, making it a crucial tool for data scientists and statisticians ²⁵.

Towards Data Science

This is part two in a series on fundamental concepts of machine learning. We discuss the basics of modeling data and fitting parameters with Maximum Likelihood Estimation.

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Towards Data Science

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