Entropy

Choosing the Right Machine Learning Model

There is no one-size-fits-all ML model. Choosing the right algorithm depends on the problem type (classification, regression, clustering), data volume, and computational resources. Traditional models like Linear Regression, Decision Trees, and Support Vector Machines work well for structured data, while deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) excel in tasks like image and speech recognition.

Model selection is an iterative process that involves testing multiple models, comparing their performance, and tuning hyperparameters for optimization.