Model Evaluation: K-Nearest Neighbor

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Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

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

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

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

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

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

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.