Data Science
607 learners
Hypertuning and Cross-Validation
Master hyperparameter tuning and cross-validation techniques to optimize the performance of your machine learning models. Learn how to perform grid search, random search, and various cross-validation methods.
Python
Scikit-learn
4 lessons
18 practices
2 hours
Model Evaluation, Validation, and Selection
Lessons and practices
Using F1 Score for Cross-Validation
Complete the Cross-Validation Process
Comparing Models Using Cross-Validation
Exploring Ensemble Models with Cross-Validation
Perform Grid Search for Model Parameters
Baking the Perfect Cake with Grid Search: Part 1
Baking the Perfect Cake with Grid Search: Part 2
Hypertune Two Models with Grid Search
Complete the Grid Search Process for Decision Tree Regressor
Tuning Iterations in Random Search
Fill in the Random Search for Best Parameters
Randomized Search for Logistic Regression Parameters
Tune the DecisionTree Classifier
Implement Model Competition
Discover Best Hyperparameters for Wine Classification
Hyperparameter Tuning for Wine Classification
Update AdaBoost
Final Challenge
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