[−][src]Crate oner_induction
The 1R (Holt, 1993) rule learning algorithm.
1R is a baseline rule learning algorithm
The algorithm generates a rule for each attribute in a dataset, and then picks the "one rule" that has the best accuracy.
Each rule (hypothesis) is a set of cases:
for every value of the attribute,
the prediction (the then
part) is the most frequent class for examples with that attribute value.
This is a baseline learner for use in comparison against more sophisticated algorithms. A related idea is "0R" (zero rule), which is the most frequent class in the dataset.
Examples
This crate uses ndarray to represent attributes and classes.
use ndarray::prelude::*; use oner_induction::{Rule, Case, Accuracy, discover}; let examples = array![ ["sunny", "summer"], ["sunny", "summer"], ["cloudy", "winter"], ["sunny", "winter"] ]; let classes = array![ "hot", "hot", "cold", "cold" ]; // Discover the best rule, and the column it applies to: let rule: Option<(usize, Rule<&str, &str>)> = discover(&examples.view(), &classes.view()); // Expected accuracy is 100% let accuracy = Accuracy(1.0); // The "rule" is a set of cases (conditions, or "IF...THENs"): let cases = vec![ Case { attribute_value: "summer", predicted_class: "hot" }, Case { attribute_value: "winter", predicted_class: "cold" } ]; // Column 1 is the Season (winter or summer) assert_eq!(rule, Some( (1, Rule { cases, accuracy }) ));
References
- Holte, R.C. (1993) Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning 11: 63. https://doi.org/10.1023/A:1022631118932.
- Molnar, C, (2019) Interpretable Machine Learning. In particular: Learn Rules from a Single Feature (OneR).
Terminology
I'm following the terminology from Holte (1993):
- Attribute (a.k.a. feature)
- Value (the value of an attribute or class)
- Class (classification, prediction)
- Example (instance)
In generic parameters, A
is for attribute and C
is for class.
Limitations
This crate assumes numeric data has already been converted to categorical data.
See https://docs.rs/oner_quantize for an implementation of the 1R qualitzation algorithm.
Structs
Accuracy | Fraction of correct predictions out of all rows in the training data. |
Case | A prediction based on an attribute value. |
Rule | The rule for an attribute, together with the training data accuracy. |
Functions
discover | Find the one rule that fits a set of example data points. |
evaluate | Evaluate cases (a.k.a., a rule) against a data set, to get a performance accuracy. |
interpret | Apply a set of cases to an attribute value to get a prediction. |