Supervised learning involves creating models that learn from input data paired with corresponding correct outputs. This chapter shifts to the practical implementation of these algorithms using the Julia programming language. You will work extensively with MLJ.jl, a primary framework in Julia designed to provide a unified interface for various machine learning tasks.

You will learn to:

• Navigate the MLJ.jl ecosystem for model building and evaluation.
• Implement and train linear models, such as linear regression for predicting continuous values and logistic regression for classification tasks.
• Construct decision tree models and understand ensemble methods like Random Forests.
• Utilize Support Vector Machines (SVMs) for classification problems.
• Assess model performance using standard evaluation metrics, including accuracy, precision, recall, and the $F_1$-score.
• Apply cross-validation techniques for more dependable model evaluation and perform hyperparameter tuning to optimize your models within MLJ.jl.

By the end of this chapter, you will be able to select, implement, train, and evaluate several common supervised learning models in Julia.