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Precision and Recall

Metrics for classification

Precision measures correctness of positive predictions, while Recall measures completeness of positive predictions.

Focus on relevant parts of input

Attention Mechanisms allow models to weigh the importance of different input elements, improving performance in NLP tasks.

Balance between precision and recall

F1 Score is the harmonic mean of precision and recall, providing a single metric for classification performance.

Architecture for sequence modeling

Transformers use self-attention to process sequences efficiently, powering models like BERT and GPT.

Assess model generalization

Cross-Validation splits data into folds to evaluate model performance on multiple subsets.

Bidirectional Encoder Representations from Transformers

BERT is a transformer-based model that understands context in both directions for NLP tasks.

Combine multiple models

Ensemble Learning improves performance by combining predictions from multiple models, such as bagging and boosting.

Gradient boosting for categorical data

CatBoost handles categorical features efficiently in gradient boosting models.

Classification algorithm

SVMs find the optimal hyperplane to separate classes in feature space.