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 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)
 
 ## 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