Interactive SkLearn Series - Permutation Importance
Meaningful interpretation. We’ll inspect model internals by shuffling feature values and measuring the drop in performance, determining which features truly drive predictions.
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12
Dec
Interactive SkLearn Series - Model Persistence
Save your work. We’ll use joblib to serialize trained models to disk, allowing you to reload them later for inference without needing to retrain from scratch.
8 min read
12
Dec
Interactive SkLearn Series - Partial Dependence Plots
9 min read
12
Dec
Interactive SkLearn Series - Text Feature Extraction
9 min read
12
Dec
Interactive SkLearn Series - Feature Selection
Less is often more. We’ll use Recursive Feature Elimination (RFE) and SelectFromModel to automatically identify and keep only the most predictive features, improving model speed.
8 min read
12
Dec
Interactive SkLearn Series - Nested Cross-Validation
The gold standard for evaluation. We’ll implement Nested CV to separate hyperparameter tuning from model evaluation, providing an unbiased estimate of generalization error.
8 min read