Principal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms data into a new coordinate system where the greatest variance lies along the first coordinates. PCA is widely used for data visualization and noise reduction.
Understanding Principal Component Analysis
Principal Component Analysis (PCA) is a statistical technique that reduces the dimensionality of high-dimensional data by identifying the directions, or principal components, along which the data varies most. By projecting data onto a smaller number of these orthogonal components, PCA preserves the maximum amount of variance while compressing the feature space, making subsequent analysis and visualization more tractable. PCA is widely used in exploratory data analysis, image compression, noise reduction, and as a preprocessing step before training machine learning models that struggle with high-dimensional inputs. For example, genomics researchers use PCA to visualize population structure from thousands of genetic markers, and computer vision systems apply it to reduce image feature dimensions. While PCA assumes linear relationships in the data, nonlinear extensions like kernel PCA and autoencoders address more complex underlying structures in representation learning tasks.
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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.