Imputation
Imputation is the process of replacing missing data values with substituted values based on statistical methods or machine learning. Proper imputation prevents biased model training from incomplete datasets.
Understanding Imputation
Imputation is the process of replacing missing or incomplete values in a dataset with estimated substitutes, enabling machine learning models to train on complete records without discarding valuable data points. Common imputation strategies range from simple approaches like mean, median, or mode filling to sophisticated techniques such as k-nearest neighbors imputation, multiple imputation by chained equations, and deep learning-based methods using masked autoencoders. The choice of imputation method can significantly impact model performance and the validity of downstream analysis. In healthcare, imputing missing patient records must be done with extreme care to avoid introducing bias. In feature stores, imputation logic is often codified as part of the feature engineering pipeline to ensure consistent handling across training and model serving. Proper imputation preserves the statistical properties of the data while minimizing the introduction of artificial patterns.
Category
Data Science
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A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.