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34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
Sure! Below is an example of a Python script demonstrating Gradient Boosting Machines (GBM) using the scikit-learn library:
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```python
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# Importing required libraries
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import accuracy_score
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# Generate a random classification dataset
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X, y = make_classification(n_samples=100, random_state=42)
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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 and fit the Gradient Boosting Classifier
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gbm_model = GradientBoostingClassifier(random_state=42)
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gbm_model.fit(X_train, y_train)
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# Predict the labels for the test set
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y_pred = gbm_model.predict(X_test)
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# Calculate the accuracy score
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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 first generate a random classification dataset using the `make_classification` function from scikit-learn. Then, we split the dataset into training and testing sets using the `train_test_split` function.
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Next, we create an instance of the Gradient Boosting Classifier using `GradientBoostingClassifier` and fit the model to the training data using the `fit` method.
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After fitting the model, we predict the labels for the test set using the `predict` method.
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Finally, we calculate the accuracy score by comparing the predicted labels with the true labels and print it out. |