Robert Tibshirani
(16 articles)
Sampling
Fundamental technique used to reduce computational cost and simplify data management
Generality: 870
Statistical Classification
The problem of identifying which category or class an object belongs to based on its features or characteristics.
Generality: 500
Supervised Classifier
Algorithm that, given a set of labeled training data, learns to predict the labels of new, unseen data.
Generality: 870
Regularization
Technique used in machine learning to reduce model overfitting by adding a penalty to the loss function based on the complexity of the model.
Generality: 845
Curse of Dimensionality
Phenomenon where the complexity and computational cost of analyzing data increase exponentially with the number of dimensions or features.
Generality: 827
Sparsity
Technique and principle of having models that utilize minimal data representation and processing, typically through zero or near-zero values.
Generality: 855
Feature Importance
Techniques used to identify and rank the significance of input variables (features) in contributing to the predictive power of a ML model.
Generality: 800
Feature Extraction
Process of transforming raw data into a set of features that are more meaningful and informative for a specific task, such as classification or prediction.
Generality: 880
Boosting
ML ensemble technique that combines multiple weak learners to form a strong learner, aiming to improve the accuracy of predictions.
Generality: 800
Similarity Computation
A mathematical process to quantify the likeness between data objects, often used in AI to enhance pattern recognition and data clustering.
Generality: 675
Ensamble Algorithm
Combines multiple machine learning models to improve overall performance by reducing bias, variance, or noise.
Generality: 860
Bias-Variance Dilemma
Fundamental problem in supervised ML that involves a trade-off between a model’s ability to minimize error due to bias and error due to variance.
Generality: 893
Ensemble Methods
ML technique where multiple models are trained and used collectively to solve a problem.
Generality: 860
Ensemble Learning
ML paradigm where multiple models (often called weak learners) are trained to solve the same problem and combined to improve the accuracy of predictions.
Generality: 795
Sparsability
Ability of algorithms to effectively handle and process data matrices where most elements are zero (sparse), improving computational efficiency and memory usage.
Generality: 675
Model-Based Classifier
ML algorithm that uses a pre-defined statistical model to make predictions based on input data.
Generality: 835