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Tree - Added Graph

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Mirrowel 2023-05-18 22:59:37 +02:00
parent 3a8cdc3e93
commit 75be644015
1 changed files with 19 additions and 11 deletions
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TreeConcept.py
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@ -1,17 +1,20 @@
from sklearn import tree from sklearn import tree
import graphviz
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
# Define the training dataset with 8 attributes and corresponding labels # Define the training dataset with 8 attributes and corresponding labels
training_data = [ training_data = [
[1, 0, 0, 1, 0, 1, 1, 'A'], [0, 0, 0, 1, 0, 1, 1, 0, 'A'],
[1, 0, 0, 0, 1, 1, 1, 'A'], [1, 0, 0, 0, 1, 1, 1, 1, 'A'],
[0, 1, 0, 1, 0, 1, 1, 'B'], [0, 1, 0, 1, 0, 1, 1, 1, 'B'],
[0, 0, 0, 1, 0, 0, 1, 'B'], [1, 0, 0, 1, 1, 0, 1, 0, 'B'],
[0, 1, 1, 0, 1, 0, 0, 'B'], [1, 1, 1, 0, 1, 0, 0, 1, 'B'],
[1, 0, 0, 0, 1, 0, 1, 'A'], [0, 0, 0, 0, 1, 1, 1, 0, 'A'],
[0, 0, 0, 1, 0, 0, 0, 'B'], [0, 0, 0, 1, 0, 0, 0, 0, 'B'],
[1, 1, 0, 1, 1, 1, 0, 'A'], [1, 1, 0, 1, 1, 1, 0, 1, 'A'],
[0, 0, 0, 0, 0, 0, 1, 'B'], [0, 0, 0, 0, 0, 0, 1, 1, 'B'],
[0, 0, 1, 0, 0, 1, 0, 'B'] [1, 0, 1, 0, 0, 1, 0, 0, 'B']
] ]
# Separate the attributes and labels # Separate the attributes and labels
@ -24,7 +27,12 @@ clf = tree.DecisionTreeClassifier(criterion='entropy')
# Train the decision tree on the training data # Train the decision tree on the training data
clf.fit(X_train, y_train) clf.fit(X_train, y_train)
# Visualize the trained decision tree
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Attr1', 'Attr2', 'Attr3', 'Attr4', 'Attr5', 'Attr6', 'Attr7', 'Attr8'], class_names=['A', 'B'], filled=True)
graph = graphviz.Source(dot_data)
graph.render("decision_tree") # Save the visualization as a PDF file
# Test the decision tree with a new example # Test the decision tree with a new example
new_example = [1, 0, 0, 1, 1, 0, 0] # Example with 8 attributes new_example = [1, 0, 0, 1, 1, 0, 0, 1] # Example with 8 attributes
predicted_label = clf.predict([new_example]) predicted_label = clf.predict([new_example])
print("Predicted Label:", predicted_label[0]) print("Predicted Label:", predicted_label[0])