Overview
Today’s focus is on improving model performance and reliability. Cross-validation helps estimate how well your model generalizes to unseen data, while GridSearchCV helps you find the best hyperparameters for your models systematically.
These tools are critical when building robust and production-ready ML models, especially in competitive settings like Kaggle.
Key Concepts
- K-Fold Cross-Validation
- Bias-variance tradeoff
- Hyperparameter tuning
- Scikit-learn’s GridSearchCV and cross_val_score
- Avoiding data leakage during CV
Practice Exercise
Exercise: Tune a Random Forest Classifier
- Use a dataset like Titanic or Iris from sklearn or Kaggle.
- Split the dataset into features and target.
- Use K-Fold CV to evaluate performance of a baseline model (e.g., RandomForestClassifier).
- Apply GridSearchCV to tune hyperparameters such as 'n_estimators', 'max_depth', and 'min_samples_split'.
- Compare results and visualize the performance across folds.
Resources
Scikit-learn GridSearchCV Documentation
Main resource for today
Cross-Validation in Scikit-learn
Tutorial on different CV techniques
Parameter Tuning Tips
Best practices for GridSearch and RandomizedSearch
Random Forest with GridSearch
Notebook showing real-world use of GridSearchCV
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