Data Science
Fixing Classical Models – Diagnosis & Regularization
In this course, learners will improve a poorly performing classical ML model using core diagnostic and regularization techniques. The model starts off weak, and learners fix it step by step through evaluation, regularization, capacity tuning, and early stopping. All models are built using scikit-learn or XGBoost.
Python
4 lessons
16 practices
1 hour
Course details
Evaluating Classification Models: Confusion Matrix and Classification Report
Fixing Models with One Parameter Change
Building a Confusion Matrix from Scratch
Calculating Classification Metrics by Hand
Fixing Misaligned Evaluation Metrics
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