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Update README.md #42

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -3,12 +3,12 @@
A Ruby library which implements [ID3 (information gain)](https://en.wikipedia.org/wiki/ID3_algorithm) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.

- Discrete model assumes unique labels & can be graphed and converted into a png for visual analysis
- Continuous looks at all possible values for a variable and iteratively chooses the best threshold between all possible assignments. This results in a binary tree which is partitioned by the threshold at every step. (e.g. temperate > 20C)
- Continuously compares all possible values for a variable and iteratively chooses the best threshold between all possible assignments. This results in a binary tree which is partitioned by the threshold at every step. (e.g. temperate > 20C)
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Previous text is correct: "Continuous" model, not "continuously"


## Features
- ID3 algorithms for continuous and discrete cases, with support for inconsistent datasets.
- [Graphviz component](http://rockit.sourceforge.net/subprojects/graphr/) to visualize the learned tree
- Support for multiple, and symbolic outputs and graphing of continuous trees.
- Support for multiple, symbolic outputs and graphing of continuous trees.
- Returns default value when no branches are suitable for input

## Implementation
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