Zero-Shot Learning
Zero-shot learning lets an AI model perform a new task or recognize an unseen category without task-specific labeled examples.
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Zero-shot learning lets an AI model perform a new task or recognize an unseen category without task-specific labeled examples.
Softmax converts a set of numerical scores into a probability distribution whose values sum to one.
k-NN predicts a new example from the labels or values of its closest stored neighbors in feature space.
Logistic regression estimates the probability of a class by applying a logistic function to a weighted combination of features.
XGBoost is a gradient-boosting algorithm that combines decision trees for accurate classification, regression, ranking, and forecasting.
Equalized odds requires a classifier to have matching true-positive and false-positive rates across protected groups.
Cross-entropy measures the difference between a predicted probability distribution and the correct target distribution.
A confusion matrix counts correct and incorrect classification results for each actual and predicted class.
A decision tree predicts an outcome by repeatedly splitting data according to feature-based rules arranged in a branching structure.
Class imbalance occurs when some target categories contain far fewer training or evaluation examples than others.
Classification is a machine learning task that assigns data to predefined categories based on patterns learned from labeled examples.
A learning approach where AI models train on labeled data with known outcomes. It powers tasks like classification, speech recognition, and predictive analytics across industries.