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Decision Tree Classifier Tutorial in Python and Scikit-Learn

Marius Borcan
Passionate software engineer since ever. Blogging at https://programmerbackpack.com/
Originally published at programmerbackpack.com ・4 min read

This article was originally published at https://programmerbackpack.com/.

Decision Tree Classifier is a classification model that can be used for simple classification tasks where the data space is not huge and can be easily visualized. Despite being simple, it is showing very good results for simple tasks and outperforms other, more complicated models.

This article is part two of a two-article mini-series on the Decision Tree Classifier. For a detailed overview of the logic and principles behind this model, please check part-one: Decision Tree Classifiers Explained.

Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.

Dataset for the Decision Tree Classifier

Recently I've created a small dummy dataset to use for simple classification tasks. I'll paste the dataset here again for your convenience.

Decision Tree Classifier - training data

The purpose of this data is, given 3 facts about a certain moment(the weather, whether it is a weekend or a workday or whether it is morning, lunch or evening), can we predict if there's a traffic jam in the city?

Decision Tree Classifier in Python with Scikit-Learn

We have 3 dependencies to install for this project, so let's install them now. Obviously, the first thing we need is the scikit-learn library, and then we need 2 more dependencies which we'll use for visualization.

pip3 install scikit-learn
pip3 install matplotlib
pip3 install pydotplus

Now let's import what we need from these packages.

from sklearn import preprocessing
from sklearn import tree
from IPython.display import Image
import pydotplus

Now let's load our dataset. In a previous article on the Naive Bayes Classifier I've defined a few helper methods to easily load our simple dataset. Let's reuse that.

def getWeather():
    return ['Clear', 'Clear', 'Clear', 'Clear', 'Clear', 'Clear',
            'Rainy', 'Rainy', 'Rainy', 'Rainy', 'Rainy', 'Rainy',
            'Snowy', 'Snowy', 'Snowy', 'Snowy', 'Snowy', 'Snowy']

def getTimeOfWeek():
    return ['Workday', 'Workday', 'Workday',
            'Weekend', 'Weekend', 'Weekend',
            'Workday', 'Workday', 'Workday',
            'Weekend', 'Weekend', 'Weekend',
            'Workday', 'Workday', 'Workday',
            'Weekend', 'Weekend', 'Weekend']

def getTimeOfDay():
    return ['Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            ]

def getTrafficJam():
    return ['Yes', 'No', 'Yes',
            'No', 'No', 'No',
            'Yes', 'Yes', 'Yes',
            'No', 'No', 'No',
            'Yes', 'Yes', 'Yes',
            'Yes', 'No', 'Yes'
            ]

Decision Tree Classifier - preprocessing

We know that computers have a really hard time when dealing with text and we can make their lives easier by converting the text to numerical values.

Label Encoder

We will use this encoder provided by scikit to transform categorical data from text to numbers. If we have n possible values in our dataset, then LabelEncoder model will transform it into numbers from 0 to n-1 so that each textual value has a number representation.

For example, let's encode our time of day values.

 timeOfDay = ['Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            'Morning', 'Lunch', 'Evening',
            ]
    labelEncoder = preprocessing.LabelEncoder()
    encodedTimeOfDay = labelEncoder.fit_transform(timeOfDay)
    print (encodedTimeOfDay)

    # Prints [2 1 0 2 1 0 2 1 0 2 1 0 2 1 0 2 1 0]

Training the Decision Tree Classifier model

Now let's train our model. So remember, since all our features are textual values, we need to encode all our values and only then we can jump to training.

if __name__=="__main__":
    # Get the data
    weather = getWeather()
    timeOfWeek = getTimeOfWeek()
    timeOfDay = getTimeOfDay()
    trafficJam = getTrafficJam()

    labelEncoder = preprocessing.LabelEncoder()

    # Encode the features and the labels
    encodedWeather = labelEncoder.fit_transform(weather)
    encodedTimeOfWeek = labelEncoder.fit_transform(timeOfWeek)
    encodedTimeOfDay = labelEncoder.fit_transform(timeOfDay)
    encodedTrafficJam = labelEncoder.fit_transform(trafficJam)

    # Build the features
    features = []
    for i in range(len(encodedWeather)):
        features.append([encodedWeather[i], encodedTimeOfWeek[i], encodedTimeOfDay[i]])

    classifier = tree.DecisionTreeClassifier()
    classifier = classifier.fit(features, encodedTrafficJam)

Using our model for predictions

Now we can use the model we have trained to make predictions about the traffic jam.

 # ["Snowy", "Workday", "Morning"]
    print(classifier.predict([[2, 1, 2]]))
    # Prints [1], meaning "Yes"
    # ["Clear", "Weekend", "Lunch"]
    print(classifier.predict([[0, 0, 1]]))
    # Prints [0], meaning "No"

And it seems to be working! It correctly predicts the traffic jam situations given our data.

Visualizing our model

Scikit also provides us with a way of visualizing a Decision Tree model. Here's a quick helper method I wrote to generate a .png image from our decision tree.

def printTree(classifier):
    feature_names = ['Weather', 'Time of Week', 'Time of Day']
    target_names = ['Yes', 'No']
    # Build the daya
    dot_data = tree.export_graphviz(classifier, out_file=None,
                                    feature_names=feature_names,
                                    class_names=target_names)
    # Build the graph
    graph = pydotplus.graph_from_dot_data(dot_data)

    # Show the image
    Image(graph.create_png())
    graph.write_png("tree.png")

And here's the result from that.

Decision Classifier - tree visualization

Yes, so I'm very happy with the results we got today. We used a really small dataset and a really small model, but I think we've learned a lot about this type of classifier.

If you want to explore more about the logic behind the Decision Tree Classifier, then please don't forget to read the other article in this mini-series: Decision Tree Classifiers Explained.

Thank you so much for reading this! Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.

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