Trevor Hastie
(24 articles)
Regression
Statistical method used in ML to predict a continuous outcome variable based on one or more predictor variables.
Generality: 860
PCA
Principal Component Analysis
Principal Component Analysis
A statistical procedure that transforms a dataset into a set of orthogonal components, intended to reduce dimensionality while preserving as much variability as possible.
Generality: 500
Cross Validation
Statistical method used to estimate the skill of ML models on unseen data by partitioning the original dataset into a training set to train the model and a test set to evaluate it.
Generality: 852
Statistical Classification
The problem of identifying which category or class an object belongs to based on its features or characteristics.
Generality: 500
Unsupervised Learning
Type of ML where algorithms learn patterns from untagged data, without any guidance on what outcomes to predict.
Generality: 905
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
Bias-Variance Trade-off
In ML, achieving optimal model performance involves balancing bias and variance to minimize overall error.
Generality: 818
Curse of Dimensionality
Phenomenon where the complexity and computational cost of analyzing data increase exponentially with the number of dimensions or features.
Generality: 827
Probabilistic Programming
Programming paradigm designed to handle uncertainty and probabilistic models, allowing for the creation of programs that can make inferences about data by incorporating statistical methods directly into the code.
Generality: 820
Empirical Risk Minimization
A foundational principle in statistics and ML (Machine Learning), focused on minimizing the average of the loss function over a sample dataset.
Generality: 814
Overfitting
When a ML model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Generality: 890
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
Meta-Classifier
Algorithm that combines multiple ML models to improve prediction accuracy over individual models.
Generality: 811
Early Stopping
A regularization technique used to prevent overfitting in ML models by halting training when performance on a validation set begins to degrade.
Generality: 675
Discriminative AI
Algorithms that learn the boundary between classes of data, focusing on distinguishing between different outputs given an input.
Generality: 840
Model-Based Classifier
ML algorithm that uses a pre-defined statistical model to make predictions based on input data.
Generality: 835