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9 min read interactive-sklearn-series

Interactive SkLearn Series - Partial Dependence Plots

Visualize cause and effect. We’ll generate Partial Dependence Plots (PDPs) to see exactly how changing a specific feature impacts the model’s predicted outcome, holding others constant.
Interactive SkLearn Series - Partial Dependence Plots

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WassupAI

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Interactive SkLearn Series - Model Persistence

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.
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Interactive SkLearn Series - Permutation Importance

Interactive SkLearn Series - Permutation Importance

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Interactive SkLearn Series - Text Feature Extraction

Interactive SkLearn Series - Text Feature Extraction

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Interactive SkLearn Series - Feature Selection

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.
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Dec
Interactive SkLearn Series - Nested Cross-Validation

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.
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