What Is Maximum Likelihood Estimation (MLE) In Logistic Regression? - The Friendly Statistician

Описание: What Is Maximum Likelihood Estimation (MLE) In Logistic Regression? Have you ever wondered how data scientists and statisticians determine the best way to interpret data and make predictions? In this informative video, we'll explain the concept of maximum likelihood estimation (MLE) and how it is used in logistic regression. We’ll start by defining what logistic regression is and how it models binary outcomes using a special curve called the logistic function. We'll discuss how the parameters of this model are estimated to best fit the observed data, allowing for accurate predictions.

We’ll also explain the role of the likelihood function and how working with its logarithm simplifies calculations. Additionally, you'll learn about the algorithms used to find the optimal coefficients that maximize the likelihood, such as gradient ascent and the Newton-Raphson method. Whether you're interested in predicting customer behavior, medical diagnoses, or financial outcomes, understanding how MLE works is essential for building reliable models.

This video is perfect for students, data enthusiasts, or professionals who want to deepen their knowledge of predictive analytics and statistical modeling. Join us to discover how maximum likelihood estimation helps create models that are closely aligned with real-world data. Don’t forget to subscribe for more insights on data analysis and machine learning techniques!

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