ML with Random Forest Training in Copenhagen, Denmark

This hands-on training provides a focused and practical introduction to machine learning using the Random Forest algorithm. Participants will learn how Random Forests work, when to use them, and how to apply them effectively for classification, regression, and feature selection tasks. The course covers both theoretical understanding and real-world implementation using Python and scikit-learn.

Foundations and Core Concepts

Introduction to Random Forests

• What is Random Forest and how it fits into ML
• Use cases: classification, regression, ranking
• Comparison with decision trees and other algorithms
• Key concepts: bootstrapping, bagging, majority voting

Decision Trees Refresher

• Splitting criteria: Gini index, entropy, variance
• Overfitting in single trees and motivation for ensembles
• Visualizing decision paths and tree depth

Building a Random Forest Model

• Constructing forests with scikit-learn
• Key parameters: number of trees, max depth, features per split
• Handling missing values and categorical data
• Out-of-bag (OOB) error and its interpretation

Advanced Topics and Real-World Applications

Regression with Random Forests

• Adapting Random Forest for regression problems
• Handling continuous outputs and evaluating with RMSE, MAE, R²
• Case study: House price or energy consumption prediction
• Visualizing prediction intervals and error spread

Feature Importance and Model Interpretability

• Feature importance using Gini and permutation methods
• Partial dependence plots and SHAP for insights
• Dealing with correlated features and dimensionality reduction

Hyperparameter Tuning and Optimization

• Grid search vs random search
• Key tuning parameters: n_estimators, max_depth, min_samples_split
• Cross-validation techniques (k-fold, stratified)
• Avoiding overfitting while improving generalization

Final Assessment and Hands-On Exercises

• Summary of learnings and real-world deployment tips