Classifying instances with Orange in Python

Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily classify data with Orange.

First we’re going to create a new data table with 10 fruits as our instances.

import Orange
from import *

color = DiscreteVariable("color", values=["orange", "green", "yellow"])
calories = ContinuousVariable("calories")
fiber = ContinuousVariable("fiber")
fruit = DiscreteVariable("fruit", values=["orange", "apple", "peach"])

domain = Domain([color, calories, fiber], class_vars=fruit)

data=Table(domain, [
["green", 4, 1.2, "apple"], 
["orange", 5, 1.1, "orange"],
["yellow", 4, 1.0, "peach"],
["orange", 4, 1.1, "orange"],
["yellow", 4, 1.1,"peach"],
["green", 5, 1.3, "apple"],
["green", 4, 1.3, "apple"],
["orange", 5, 1.0, "orange"],
["yellow", 4.5, 1.3, "peach"],
["green", 5, 1.0, "orange"]])


Now we have to select a model for classification. Among the many learners in Orange library, we decided to use the Tree Learner for this example. Since we’re dealing with fruits, we thought it’s only appropriate. 🙂

Let’s create a learning algorithm and use it to induce the classifier from the data.

tree_learner = Orange.classification.TreeLearner()
tree = tree_learner(data)

Now we can predict what variety a green fruit with 3.5 calories and 2g of fiber is with the help of our model. To do this, simply call the model and use a list of new data as argument.

print(tree(["green", 3.5, 2]))

Python returns index as a result:


To check the index, we can call class variable values with the corresponding index:


Final result:


You can use your own data set to see how this model works for different data types. Let us know how it goes! 🙂