Concepts# These explanations dive into the what, why and theory behind each of DataEval’s features. Bayes Error Rate Estimation Cluster Assignment Coverage Analysis Detecting Bias in Datasets Cleaning Datasets Evaluating Parity Evaluating Label Parity Dataset Divergence Detecting Drift in Datasets Detecting Duplicates Image Statistics Functions Outliers Class Model Training Techniques Detecting Out of Distribution Data Sufficiency of Datasets per Model Upperbound on Average Precision Estimation Balance Bias Metrics Diversity Indices
