Model Evaluation

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Related lectures (30)

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Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

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Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Model Selection: Generalization and Validation

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.