Multi-Class Classification: Approaches and Boundaries

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Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers learning bounds, complexities, growth function, shattering, and VC dimension in binary classifiers.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Covers classification problems and various loss functions used in machine learning.

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.