cyber-security-resources/ai_research/ML_Fundamentals/ai_generated/data/ai_generated_python_scripts/Gradient_Boosting_Machines_(GBM).md

Sure! Below is an example of a Python script demonstrating Gradient Boosting Machines (GBM) using the scikit-learn library:

# Importing required libraries
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score

# Generate a random classification dataset
X, y = make_classification(n_samples=100, random_state=42)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and fit the Gradient Boosting Classifier
gbm_model = GradientBoostingClassifier(random_state=42)
gbm_model.fit(X_train, y_train)

# Predict the labels for the test set
y_pred = gbm_model.predict(X_test)

# Calculate the accuracy score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

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.

Next, we create an instance of the Gradient Boosting Classifier using GradientBoostingClassifier and fit the model to the training data using the fit method.

After fitting the model, we predict the labels for the test set using the predict method.

Finally, we calculate the accuracy score by comparing the predicted labels with the true labels and print it out.