Traditional Machine Learning in Practice: Learning Paradigms, Algorithm Families, and Evaluation Perspectives

📰 Dev.to · shangkyu shin

Learn the fundamentals of traditional machine learning, including learning paradigms, algorithm families, and evaluation perspectives, to improve your ML practice

intermediate Published 11 Apr 2026

Action Steps

Explore the different learning paradigms, such as supervised, unsupervised, and reinforcement learning, to determine which one suits your problem
Identify the algorithm families, including regression, classification, clustering, and dimensionality reduction, to choose the right tool for your task
Evaluate your models using various evaluation perspectives, such as accuracy, precision, recall, and F1 score, to assess their performance
Apply cross-validation techniques to ensure the reliability of your model's performance estimates
Compare the strengths and weaknesses of different algorithm families to select the most suitable one for your problem

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this guide to improve their understanding of traditional machine learning and develop more effective models

Key Insight

💡 Understanding the learning paradigms, algorithm families, and evaluation perspectives is crucial for developing effective traditional machine learning models