Choosing the Right Machine Learning Model: When & Why?
Selecting the right machine learning model depends on the problem, data type, interpretability, and computational constraints. Below is a structured guide explaining which model to use, why it makes sense, why other models might not be suitable, and real-world examples. 1. Supervised Learning (Labeled Data) Classification (Predicting Categories) 1. Logistic Regression - Use When : The data is linearly separable, and interpretability is important. - Why It Makes Sense : Outputs probabilities, making it useful for decision-making. - Why Others Don't : - Decision Trees & Random Forest can overfit on small datasets. - SVM may be overkill for simple problems. - Neural Networks require large data and are computationally expensive. - Real-World Example : Credit Scoring Systems – Banks use logistic regression to predict whether a borrower will default on a loan. ...