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CodeSignal

Hypertuning and Cross-Validation

via CodeSignal

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Overview

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.

Syllabus

  • Unit 1: Cross-Validation in Machine Learning
    • Using F1 Score for Cross-Validation
    • Complete the Cross-Validation Process
    • Comparing Models Using Cross-Validation
    • Exploring Ensemble Models with Cross-Validation
  • Unit 2: Grid Search: Finding Optimal Model Parameters
    • 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
  • Unit 3: Random Search in Machine Learning
    • 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
  • Unit 4: Hyperparameter Tuning for Ensembles
    • Discover Best Hyperparameters for Wine Classification
    • Hyperparameter Tuning for Wine Classification
    • Update AdaBoost
    • Final Challenge

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