- (Kohavi & Provost, 1998) ⇒ Ron Kohavi, Foster Provost. (1998). “Glossary of Terms.” In: Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, Machine Learning, 30(2-3). DOI:10.1023/A:1017181826899
Subject Headings: Data Mining Glossary.
- HTML Version: http://robotics.stanford.edu/~ronnyk/glossary.html
- Provides Informal Concept Definitions for:
- Accuracy, Association Learning, Attribute, Case, Categorical, Classifier, Confusion Matrix, Cost, Coverage, Cross-Validation, Data Cleaning, Data Cleansing, Data Mining, Data Set, Dimension, Error, Error Rate, Example, False Negative Rate, False Positive Rate, Feature, Feature Vector, Field, IID Sample, Inducer, Induction Algorithm, Instance, Knowledge Discovery, Loss, Machine Learning, Missing Value, Model, Model Deployment, MOLAP, OLAP, Payoff, Precision, Quantitative, Recall, Record, Regressor, Resubstitution Accuracy, ROLAP, Schema, Sensitivity, Specificity, Supervised Learning, True Negative Rate, True Positive Rate, Tuple, Unsupervised Learning, Utility, and Variable.
To help readers understand common terms in machine learning, statistics, and data mining, we provide a glossary of common terms. The definitions are not designed to be completely general, but instead are aimed at the most common case.
- Regressor: A mapping from unlabeled instances to a value within a predefined metric space (e.g., a continuous range).