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CodeSignal

Model Evaluation and Optimization

via CodeSignal

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Overview

Any predictive regression model is only as good as its performance, this course delves into advanced techniques for evaluating and optimizing regression models. Explore sophisticated strategies to enhance predictive accuracy and model robustness.

Syllabus

  • Unit 1: Advanced Regression Model Evaluation Techniques
    • Evaluating the Performance of a Forecast Model
    • Regression Metrics Accuracy Check
    • Exploring the Impact of Noise on Advanced Evaluation Metrics
    • Calculating Regression Model Evaluation Metrics
    • Implementing Advanced Regression Metrics
  • Unit 2: Navigating the Seas of Data: Mastering Cross-Validation in Python
    • Understanding Cross-Validation in Practice
    • Adjusting the Number of K-Fold Splits
    • Debugging Cross-Validation in Housing Price Prediction
    • Conjuring Cross-Validation with KFold
    • Cross-Validation Mastery with California Housing Data
  • Unit 3: Optimizing Machine Learning Models with Hyperparameter Tuning
    • Tuning Hyperparameters with Grid Search
    • Hyperparameter Tuning: Expanding the Neural Network Structure
    • Tuning Model Performance with GridSearchCV
    • Hyperparameter Space Odyssey
  • Unit 4: Mastering Regularization in Machine Learning with Ridge and Lasso Techniques
    • Exploring the Effects of Regularization Techniques
    • Adjusting Alpha: Regularization in Action
    • Setting the Course with Regularization Parameters
    • Charting the Course: Regularization in Regression

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