Use case
Given that the Iris Data Set as the following
| Sepal length | Sepal width | Petal length | Petal width | Species |
|---|---|---|---|---|
| 6.8 | 3.2 | 5.9 | 2.3 | virginica |
| 5.1 | 3.5 | 1.4 | 0.3 | setosa |
| 4.4 | 3.2 | 1.3 | 0.2 | setosa |
| 5.8 | 4 | 1.2 | 0.2 | setosa |
| 6.3 | 3.3 | 6 | 2.5 | virginica |
| 5.7 | 4.4 | 1.5 | 0.4 | setosa |
| 6.2 | 2.2 | 4.5 | 1.5 | versicolor |
| 7.1 | 3 | 5.9 | 2.1 | virginica |
| 6.4 | 2.8 | 5.6 | 2.1 | virginica |
| 6 | 2.9 | 4.5 | 1.5 | versicolor |
| 5.4 | 3.9 | 1.3 | 0.4 | setosa |
| 6.1 | 2.8 | 4 | 1.3 | versicolor |
| 4.4 | 3 | 1.3 | 0.2 | setosa |
| 5.5 | 4.2 | 1.4 | 0.2 | setosa |
| 6.1 | 3 | 4.6 | 1.4 | versicolor |
| 5.9 | 3 | 5.1 | 1.8 | virginica |
| 4.6 | 3.2 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.6 | 0.2 | setosa |
| 4.8 | 3 | 1.4 | 0.1 | setosa |
| 5.6 | 2.5 | 3.9 | 1.1 | versicolor |
| 5.1 | 3.4 | 1.5 | 0.2 | setosa |
| 5.1 | 3.8 | 1.5 | 0.3 | setosa |
| 7.9 | 3.8 | 6.4 | 2 | virginica |
| 6.3 | 2.5 | 5 | 1.9 | virginica |
| 6.5 | 3 | 5.2 | 2 | virginica |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
| 5.7 | 2.5 | 5 | 2 | virginica |
| 5.5 | 2.5 | 4 | 1.3 | versicolor |
| 5.8 | 2.8 | 5.1 | 2.4 | virginica |
| 5.4 | 3 | 4.5 | 1.5 | versicolor |
| 7.6 | 3 | 6.6 | 2.1 | virginica |
| 7.7 | 2.8 | 6.7 | 2 | virginica |
| 5.6 | 2.8 | 4.9 | 2 | virginica |
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 6.3 | 3.3 | 4.7 | 1.6 | versicolor |
| 6.2 | 2.8 | 4.8 | 1.8 | virginica |
| 4.9 | 3.6 | 1.4 | 0.1 | setosa |
| 6.3 | 2.5 | 4.9 | 1.5 | versicolor |
| 4.8 | 3.1 | 1.6 | 0.2 | setosa |
| 6.1 | 2.8 | 4.7 | 1.2 | versicolor |
| 5.1 | 3.3 | 1.7 | 0.5 | setosa |
| 6.9 | 3.1 | 5.4 | 2.1 | virginica |
| 6.1 | 3 | 4.9 | 1.8 | virginica |
| 5.1 | 3.7 | 1.5 | 0.4 | setosa |
| 6.8 | 2.8 | 4.8 | 1.4 | versicolor |
| 5.4 | 3.4 | 1.7 | 0.2 | setosa |
| 6.8 | 3 | 5.5 | 2.1 | virginica |
| 5.6 | 2.9 | 3.6 | 1.3 | versicolor |
| 5.1 | 2.5 | 3 | 1.1 | versicolor |
| 6.5 | 3 | 5.5 | 1.8 | virginica |
| 6.5 | 2.8 | 4.6 | 1.5 | versicolor |
| 5.8 | 2.7 | 5.1 | 1.9 | virginica |
| 6.5 | 3.2 | 5.1 | 2 | virginica |
| 6.9 | 3.1 | 4.9 | 1.5 | versicolor |
| 5 | 3 | 1.6 | 0.2 | setosa |
| 5.6 | 2.7 | 4.2 | 1.3 | versicolor |
| 6.3 | 2.8 | 5.1 | 1.5 | virginica |
| 5 | 3.4 | 1.5 | 0.2 | setosa |
| 5.7 | 3 | 4.2 | 1.2 | versicolor |
| 6.4 | 2.7 | 5.3 | 1.9 | virginica |
| 4.9 | 3.1 | 1.5 | 0.2 | setosa |
| 5.2 | 4.1 | 1.5 | 0.1 | setosa |
| 6.4 | 3.1 | 5.5 | 1.8 | virginica |
| 6.4 | 2.8 | 5.6 | 2.2 | virginica |
| 5.7 | 2.8 | 4.5 | 1.3 | versicolor |
| 6 | 3.4 | 4.5 | 1.6 | versicolor |
| 5.8 | 2.7 | 4.1 | 1 | versicolor |
| 6.9 | 3.2 | 5.7 | 2.3 | virginica |
| 6.4 | 3.2 | 5.3 | 2.3 | virginica |
| 5 | 3.5 | 1.6 | 0.6 | setosa |
| 7 | 3.2 | 4.7 | 1.4 | versicolor |
| 7.2 | 3 | 5.8 | 1.6 | virginica |
| 5.3 | 3.7 | 1.5 | 0.2 | setosa |
| 5.6 | 3 | 4.1 | 1.3 | versicolor |
| 4.8 | 3 | 1.4 | 0.3 | setosa |
| 6.7 | 3.3 | 5.7 | 2.1 | virginica |
| 5.2 | 3.4 | 1.4 | 0.2 | setosa |
| 6.9 | 3.1 | 5.1 | 2.3 | virginica |
| 5.1 | 3.8 | 1.9 | 0.4 | setosa |
| 6.2 | 2.9 | 4.3 | 1.3 | versicolor |
| 6.4 | 2.9 | 4.3 | 1.3 | versicolor |
| 7.7 | 3.8 | 6.7 | 2.2 | virginica |
| 6.6 | 2.9 | 4.6 | 1.3 | versicolor |
| 5 | 3.5 | 1.3 | 0.3 | setosa |
| 4.4 | 2.9 | 1.4 | 0.2 | setosa |
| 5.8 | 2.7 | 5.1 | 1.9 | virginica |
| 5.5 | 2.6 | 4.4 | 1.2 | versicolor |
| 6.3 | 3.4 | 5.6 | 2.4 | virginica |
| 6.7 | 3 | 5 | 1.7 | versicolor |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 5 | 3.3 | 1.4 | 0.2 | setosa |
| 5.8 | 2.7 | 3.9 | 1.2 | versicolor |
| 4.3 | 3 | 1.1 | 0.1 | setosa |
| 6.6 | 3 | 4.4 | 1.4 | versicolor |
| 6.7 | 2.5 | 5.8 | 1.8 | virginica |
| 5.7 | 2.6 | 3.5 | 1 | versicolor |
| 4.9 | 3 | 1.4 | 0.2 | setosa |
| 5.7 | 3.8 | 1.7 | 0.3 | setosa |
| 4.9 | 2.5 | 4.5 | 1.7 | virginica |
| 5.1 | 3.8 | 1.6 | 0.2 | setosa |
The requirement is predicting the species of data in the table below
| Sepal length | Sepal width | Petal length | Petal width | Species |
|---|---|---|---|---|
| 6.3 | 2.3 | 4.4 | 1.3 | ? |
| 6 | 3 | 4.8 | 1.8 | ? |
| 5.9 | 3.2 | 4.8 | 1.8 | ? |
| 6.7 | 3.1 | 4.7 | 1.5 | ? |
| 4.6 | 3.1 | 1.5 | 0.2 | ? |
| 6.1 | 2.6 | 5.6 | 1.4 | ? |
| 4.6 | 3.4 | 1.4 | 0.3 | ? |
| 7.7 | 3 | 6.1 | 2.3 | ? |
| 5.7 | 2.9 | 4.2 | 1.3 | ? |
| 5 | 3.2 | 1.2 | 0.2 | ? |
| 5.4 | 3.4 | 1.5 | 0.4 | ? |
| 5 | 3.6 | 1.4 | 0.2 | ? |
| 7.7 | 2.6 | 6.9 | 2.3 | ? |
| 5.2 | 3.5 | 1.5 | 0.2 | ? |
| 5.5 | 2.3 | 4 | 1.3 | ? |
| 5.6 | 3 | 4.5 | 1.5 | ? |
| 6 | 2.2 | 4 | 1 | ? |
| 4.9 | 3.1 | 1.5 | 0.1 | ? |
| 4.8 | 3.4 | 1.9 | 0.2 | ? |
| 6.3 | 2.7 | 4.9 | 1.8 | ? |
| 6.7 | 3.1 | 4.4 | 1.4 | ? |
| 5.2 | 2.7 | 3.9 | 1.4 | ? |
| 5.9 | 3 | 4.2 | 1.5 | ? |
| 5.4 | 3.7 | 1.5 | 0.2 | ? |
| 7.2 | 3.2 | 6 | 1.8 | ? |
| 6 | 2.2 | 5 | 1.5 | ? |
| 6.7 | 3.1 | 5.6 | 2.4 | ? |
| 6.2 | 3.4 | 5.4 | 2.3 | ? |
| 5.5 | 2.4 | 3.7 | 1 | ? |
| 6.3 | 2.9 | 5.6 | 1.8 | ? |
| 6.4 | 3.2 | 4.5 | 1.5 | ? |
| 4.6 | 3.6 | 1 | 0.2 | ? |
| 6.7 | 3 | 5.2 | 2.3 | ? |
| 5.5 | 3.5 | 1.3 | 0.2 | ? |
| 5.8 | 2.6 | 4 | 1.2 | ? |
| 6.5 | 3 | 5.8 | 2.2 | ? |
| 4.8 | 3.4 | 1.6 | 0.2 | ? |
| 6.7 | 3.3 | 5.7 | 2.5 | ? |
| 5.5 | 2.4 | 3.8 | 1.1 | ? |
| 5.7 | 2.8 | 4.1 | 1.3 | ? |
| 5 | 3.4 | 1.6 | 0.4 | ? |
| 6.1 | 2.9 | 4.7 | 1.4 | ? |
| 4.9 | 2.4 | 3.3 | 1 | ? |
| 7.4 | 2.8 | 6.1 | 1.9 | ? |
| 6 | 2.7 | 5.1 | 1.6 | ? |
| 4.5 | 2.3 | 1.3 | 0.3 | ? |
| 5 | 2 | 3.5 | 1 | ? |
| 7.2 | 3.6 | 6.1 | 2.5 | ? |
| 5 | 2.3 | 3.3 | 1 | ? |
| 7.3 | 2.9 | 6.3 | 1.8 | ? |
How to resolve this issue using Gaussian Naive Bayes
Install gem
gem install gaussian_naive_bayes
Prepare the training set
training_set = [[6.8,3.2,5.9,2.3],[5.1,3.5,1.4,0.3],[4.4,3.2,1.3,0.2],[5.8,4,1.2,0.2],[6.3,3.3,6,2.5],[5.7,4.4,1.5,0.4],[6.2,2.2,4.5,1.5],[7.1,3,5.9,2.1],[6.4,2.8,5.6,2.1],[6,2.9,4.5,1.5],[5.4,3.9,1.3,0.4],[6.1,2.8,4,1.3],[4.4,3,1.3,0.2],[5.5,4.2,1.4,0.2],[6.1,3,4.6,1.4],[5.9,3,5.1,1.8],[4.6,3.2,1.4,0.2],[4.7,3.2,1.6,0.2],[4.8,3,1.4,0.1],[5.6,2.5,3.9,1.1],[5.1,3.4,1.5,0.2],[5.1,3.8,1.5,0.3],[7.9,3.8,6.4,2],[6.3,2.5,5,1.9],[6.5,3,5.2,2],[5.4,3.9,1.7,0.4],[5.7,2.5,5,2],[5.5,2.5,4,1.3],[5.8,2.8,5.1,2.4],[5.4,3,4.5,1.5],[7.6,3,6.6,2.1],[7.7,2.8,6.7,2],[5.6,2.8,4.9,2],[5.1,3.5,1.4,0.2],[6.3,3.3,4.7,1.6],[6.2,2.8,4.8,1.8],[4.9,3.6,1.4,0.1],[6.3,2.5,4.9,1.5],[4.8,3.1,1.6,0.2],[6.1,2.8,4.7,1.2],[5.1,3.3,1.7,0.5],[6.9,3.1,5.4,2.1],[6.1,3,4.9,1.8],[5.1,3.7,1.5,0.4],[6.8,2.8,4.8,1.4],[5.4,3.4,1.7,0.2],[6.8,3,5.5,2.1],[5.6,2.9,3.6,1.3],[5.1,2.5,3,1.1],[6.5,3,5.5,1.8],[6.5,2.8,4.6,1.5],[5.8,2.7,5.1,1.9],[6.5,3.2,5.1,2],[6.9,3.1,4.9,1.5],[5,3,1.6,0.2],[5.6,2.7,4.2,1.3],[6.3,2.8,5.1,1.5],[5,3.4,1.5,0.2],[5.7,3,4.2,1.2],[6.4,2.7,5.3,1.9],[4.9,3.1,1.5,0.2],[5.2,4.1,1.5,0.1],[6.4,3.1,5.5,1.8],[6.4,2.8,5.6,2.2],[5.7,2.8,4.5,1.3],[6,3.4,4.5,1.6],[5.8,2.7,4.1,1],[6.9,3.2,5.7,2.3],[6.4,3.2,5.3,2.3],[5,3.5,1.6,0.6],[7,3.2,4.7,1.4],[7.2,3,5.8,1.6],[5.3,3.7,1.5,0.2],[5.6,3,4.1,1.3],[4.8,3,1.4,0.3],[6.7,3.3,5.7,2.1],[5.2,3.4,1.4,0.2],[6.9,3.1,5.1,2.3],[5.1,3.8,1.9,0.4],[6.2,2.9,4.3,1.3],[6.4,2.9,4.3,1.3],[7.7,3.8,6.7,2.2],[6.6,2.9,4.6,1.3],[5,3.5,1.3,0.3],[4.4,2.9,1.4,0.2],[5.8,2.7,5.1,1.9],[5.5,2.6,4.4,1.2],[6.3,3.4,5.6,2.4],[6.7,3,5,1.7],[4.7,3.2,1.3,0.2],[5,3.3,1.4,0.2],[5.8,2.7,3.9,1.2],[4.3,3,1.1,0.1],[6.6,3,4.4,1.4],[6.7,2.5,5.8,1.8],[5.7,2.6,3.5,1],[4.9,3,1.4,0.2],[5.7,3.8,1.7,0.3],[4.9,2.5,4.5,1.7],[5.1,3.8,1.6,0.2]]
target_category = ["virginica","setosa","setosa","setosa","virginica","setosa","versicolor","virginica","virginica","versicolor","setosa","versicolor","setosa","setosa","versicolor","virginica","setosa","setosa","setosa","versicolor","setosa","setosa","virginica","virginica","virginica","setosa","virginica","versicolor","virginica","versicolor","virginica","virginica","virginica","setosa","versicolor","virginica","setosa","versicolor","setosa","versicolor","setosa","virginica","virginica","setosa","versicolor","setosa","virginica","versicolor","versicolor","virginica","versicolor","virginica","virginica","versicolor","setosa","versicolor","virginica","setosa","versicolor","virginica","setosa","setosa","virginica","virginica","versicolor","versicolor","versicolor","virginica","virginica","setosa","versicolor","virginica","setosa","versicolor","setosa","virginica","setosa","virginica","setosa","versicolor","versicolor","virginica","versicolor","setosa","setosa","virginica","versicolor","virginica","versicolor","setosa","setosa","versicolor","setosa","versicolor","virginica","versicolor","setosa","setosa","virginica","setosa"]
Train
require "gaussian_naive_bayes"
learner = GaussianNaiveBayes::Learner.new
training_set.each_with_index do |vector, index|
learner.train(vector, target_category[index])
end
Prepare the test set
test_set = [[6.3,2.3,4.4,1.3],[6,3,4.8,1.8],[5.9,3.2,4.8,1.8],[6.7,3.1,4.7,1.5],[4.6,3.1,1.5,0.2],[6.1,2.6,5.6,1.4],[4.6,3.4,1.4,0.3],[7.7,3,6.1,2.3],[5.7,2.9,4.2,1.3],[5,3.2,1.2,0.2],[5.4,3.4,1.5,0.4],[5,3.6,1.4,0.2],[7.7,2.6,6.9,2.3],[5.2,3.5,1.5,0.2],[5.5,2.3,4,1.3],[5.6,3,4.5,1.5],[6,2.2,4,1],[4.9,3.1,1.5,0.1],[4.8,3.4,1.9,0.2],[6.3,2.7,4.9,1.8],[6.7,3.1,4.4,1.4],[5.2,2.7,3.9,1.4],[5.9,3,4.2,1.5],[5.4,3.7,1.5,0.2],[7.2,3.2,6,1.8],[6,2.2,5,1.5],[6.7,3.1,5.6,2.4],[6.2,3.4,5.4,2.3],[5.5,2.4,3.7,1],[6.3,2.9,5.6,1.8],[6.4,3.2,4.5,1.5],[4.6,3.6,1,0.2],[6.7,3,5.2,2.3],[5.5,3.5,1.3,0.2],[5.8,2.6,4,1.2],[6.5,3,5.8,2.2],[4.8,3.4,1.6,0.2],[6.7,3.3,5.7,2.5],[5.5,2.4,3.8,1.1],[5.7,2.8,4.1,1.3],[5,3.4,1.6,0.4],[6.1,2.9,4.7,1.4],[4.9,2.4,3.3,1],[7.4,2.8,6.1,1.9],[6,2.7,5.1,1.6],[4.5,2.3,1.3,0.3],[5,2,3.5,1],[7.2,3.6,6.1,2.5],[5,2.3,3.3,1],[7.3,2.9,6.3,1.8]]
Classify
classifier = learner.classifier
p classifier.classify([6.3,2.3,4.4,1.3]) # => "versicolor"
p classifier.classify([6,3,4.8,1.8]) # => "virginica"