Decision Tree Classifier #3: Complete Coding with SciKitLearn

Decision Tree Classifier is a popular algorithm used in machine learning for classification tasks. In this article, we will walk through the complete coding of a Decision Tree Classifier using the SciKitLearn library in Python.

Step 1: Importing the Required Libraries

First, we need to import the necessary libraries for our coding. We will be using SciKitLearn for implementing the Decision Tree Classifier, so we need to import the ‘tree’ module from the library.

“`
import numpy as np
from sklearn import tree
“`

Step 2: Loading the Data

Next, we need to load the dataset that we will be using for training and testing our Decision Tree Classifier. For this example, let’s assume we have a dataset with features and labels. We can load the features into a variable ‘X’ and the labels into a variable ‘y’.

“`
X = [[0, 0], [1, 1]]
y = [0, 1]
“`

Step 3: Creating and Training the Decision Tree Classifier

Now, we can create an instance of the Decision Tree Classifier and train it using our dataset.

“`
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)
“`

Step 4: Making Predictions

Once the classifier is trained, we can use it to make predictions on new data. We can use the ‘predict’ method of the classifier to predict the label for a new set of features.

“`
print(clf.predict([[2., 2.]]))
“`

Step 5: Visualizing the Decision Tree

We can also visualize the decision tree that was created by our classifier using the ‘export_graphviz’ method from the ‘tree’ module and a visualization tool like Graphviz.

“`
import graphviz
dot_data = tree.export_graphviz(clf, out_file=None)
graph = graphviz.Source(dot_data)
graph.render(“decision_tree”)
“`

By following these steps, we have successfully implemented a Decision Tree Classifier using the SciKitLearn library in Python. We have loaded the data, trained the classifier, made predictions, and visualized the decision tree. This coding example provides a clear and concise demonstration of using the Decision Tree Classifier for classification tasks.