From 9d750fa10e0b72378329a07dead4392e48d24704 Mon Sep 17 00:00:00 2001 From: vchen30 Date: Fri, 30 Mar 2018 04:02:30 -0700 Subject: [PATCH] Update README.md notice you forgot to made a word adverb and suggested a better word than "look at" --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index adb1c41..4791d9e 100644 --- a/README.md +++ b/README.md @@ -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) ## 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