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38 lines
1.8 KiB
Python
38 lines
1.8 KiB
Python
Sure! Here's an example script demonstrating the use of Support Vector Machines (SVM) for a binary classification problem using the famous iris dataset:
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```python
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# Importing the required libraries
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.svm import SVC
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from sklearn.metrics import accuracy_score
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# Loading the iris dataset
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iris = datasets.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=0.3, random_state=42)
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# Creating the SVM classifier
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svm = SVC(kernel='linear')
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# Training the SVM classifier
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svm.fit(X_train, y_train)
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# Making predictions on the testing set
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y_pred = svm.predict(X_test)
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# Calculating the accuracy of the model
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accuracy = accuracy_score(y_test, y_pred)
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print(f"Accuracy: {accuracy}")
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```
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In this script, the iris dataset is first loaded using the `datasets.load_iris()` method from the sklearn library. The features matrix `X` and the target vector `y` are then initialized. Next, the dataset is split into training and testing sets using `train_test_split()` function from the `model_selection` module.
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A Support Vector Machine classifier is created using `SVC()` class from the `svm` module, with the `kernel` parameter set to 'linear'. The classifier is then trained on the training set using the `fit()` method.
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Predictions are made on the testing set using the `predict()` method, and the accuracy of the model is calculated using the `accuracy_score()` function from the `metrics` module. Finally, the accuracy is printed to the console.
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You can modify this script according to your specific use case or dataset. Remember to install the required packages (`scikit-learn`) in your Python environment before running the script. |