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34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
Sure! Here's a python script that demonstrates the concept of Random Forests using the famous Iris dataset.
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```python
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# Importing the required libraries
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from sklearn.ensemble import RandomForestClassifier
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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.metrics import accuracy_score
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# Loading the Iris dataset
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iris = load_iris()
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X = iris.data
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y = iris.target
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# Splitting 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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# Creating a Random Forest classifier
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rf_classifier = RandomForestClassifier(n_estimators=100)
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# Training the classifier
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rf_classifier.fit(X_train, y_train)
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# Predicting on the test set
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y_pred = rf_classifier.predict(X_test)
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# Calculating accuracy
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accuracy = accuracy_score(y_test, y_pred)
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print("Accuracy:", accuracy)
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```
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In this script, we import the necessary libraries, load the Iris dataset, and split it into training and testing sets. Then, we create a Random Forest classifier with 100 decision trees (`n_estimators=100`) and train it on the training set. Finally, we predict the target variable for the test set and calculate the accuracy of the classifier.
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Note that you may need to install scikit-learn (`pip install scikit-learn`) if you don't have it already installed. |