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44 lines
1.8 KiB
Python
44 lines
1.8 KiB
Python
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Sure! Below is a Python script that demonstrates the concept of Decision Trees using the popular scikit-learn library.
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```python
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# Import necessary libraries
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from sklearn import metrics
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# Load the Iris dataset
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data = load_iris()
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X = data.data
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y = data.target
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# Split the dataset into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Create a Decision Tree classifier
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clf = DecisionTreeClassifier()
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# Train the classifier on the training data
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clf.fit(X_train, y_train)
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# Make predictions on the testing data
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y_pred = clf.predict(X_test)
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# Evaluate the model
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accuracy = metrics.accuracy_score(y_test, y_pred)
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print("Accuracy:", accuracy)
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# Visualize the Decision Tree
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from sklearn import tree
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import matplotlib.pyplot as plt
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plt.figure(figsize=(12, 8))
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tree.plot_tree(clf, feature_names=data.feature_names, class_names=data.target_names, filled=True)
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plt.show()
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```
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In this script, we first import the necessary libraries: `load_iris` from `sklearn.datasets` to load the Iris dataset, `train_test_split` from `sklearn.model_selection` to split the dataset into training and testing sets, `DecisionTreeClassifier` from `sklearn.tree` to create the Decision Tree classifier, and `metrics` from `sklearn` to evaluate the model.
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We load the Iris dataset and split it into training and testing sets using a 80:20 split. Then, we create a Decision Tree classifier and train it on the training data. After that, we make predictions on the testing data and evaluate the model using accuracy as the metric.
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Finally, we visualize the Decision Tree using `tree.plot_tree` from `sklearn` and `matplotlib.pyplot`. The resulting tree is displayed using a figure.
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