Daily Dose of Data Science | Avi Chawla | Substack

This post highlights the most commonly used loss functions in regression and classification tasks. It covers Mean Bias Error, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Huber Loss, and Log Cosh Loss for regression. For classification, it discusses Binary Cross Entropy, Hinge Loss, Cross-Entropy Loss, and KL Divergence. Each loss function is briefly explained along with its pros and cons.

10 Regression and Classification Loss Functions

The post explains the fundamental differences between discriminative and generative models in machine learning. Discriminative models focus on learning decision boundaries for classification by maximizing the conditional probability P(Y|X), with examples like logistic regression and decision trees. Generative models, such as Naive Bayes and Gaussian Mixture Models, learn the joint probability P(X, Y) and can generate new samples. A quiz is included to further illustrate the concepts and differentiate between the two approaches.

A Popular Interview Question: Discriminative vs. Generative Models

Delve into classification algorithms and machine learning techniques for categorizing data into predefined classes or categories. Learn about supervised learning, classification models, and evaluation metrics. Whether you're a data scientist, researcher, or machine learning enthusiast,  understand and implement classification algorithms.

Best of Classification — August 2024