Machine Learning
537 learners
Model Evaluation and Optimization
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.
Pandas
Python
Scikit-learn
See path
4 lessons
18 practices
2 hours
Badge for Model Validation and Selection,
Model Validation and Selection
Lessons and practices
Lesson 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
Lesson 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
Lesson 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
Lesson 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
Meet Cosmo:
The smartest AI guide in the universe
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.
Sign up
Join the 1M+ learners on CodeSignal
Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal