Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.
Explores out-of-sample validation and the methodology of cross-validation for testing predictive models.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Explores the implementation of Connect Four game functionalities and testing methods.
Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.
