Feature Engineering
Feature engineering is the process of creating, selecting, and transforming input variables to improve machine learning model performance. Good feature engineering often matters more than model choice for traditional ML tasks.
Understanding Feature Engineering
Feature engineering is the process of selecting, transforming, and creating input variables from raw data to improve the performance of machine learning models. Effective features capture the underlying patterns relevant to a prediction task, and skilled feature engineering can often outperform gains from more complex model architectures. Common techniques include one-hot encoding categorical variables, normalizing numerical features, creating interaction terms, extracting date components, and applying domain-specific transformations. In traditional machine learning with algorithms like decision trees and logistic regression, feature engineering is critical, while deep learning models perform some automatic feature extraction through their hidden layers. Feature engineering interacts closely with data pipeline design and dimensionality reduction, as too many features can introduce noise and increase computation. Tools like Featuretools automate parts of this process, and feature importance analysis guides iterative refinement.
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.