Leo Breiman
(16 articles)
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
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
Predictive Analytics
Using statistical techniques and algorithms to analyze historical data and make predictions about future events.
Generality: 874
Decision Tree
Flowchart-like tree structure where each internal node represents a
Generality: 851
Bias-Variance Trade-off
In ML, achieving optimal model performance involves balancing bias and variance to minimize overall error.
Generality: 818
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
Boosting
ML ensemble technique that combines multiple weak learners to form a strong learner, aiming to improve the accuracy of predictions.
Generality: 800
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
Bagging
ML ensemble technique that improves the stability and accuracy of machine learning algorithms by combining multiple models trained on different subsets of the same data set.
Generality: 835
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
Out-of-Bag Evaluation
A technique for assessing the performance and accuracy of ensemble models, particularly in random forests, using validation data derived from bootstrap sampling without additional data partitioning.
Generality: 500
Random Forest
Robust ML algorithm that combines multiple decision trees to improve prediction accuracy and prevent overfitting.
Generality: 810