xgboost sklearn in python

To use XGBoost with scikit-learn in Python, you need to follow these steps:

1. Install the required dependencies:

   main.py
   pip install xgboost
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2. Import the necessary libraries:

   main.py
   import xgboost as xgb
   from sklearn.model_selection import train_test_split
   from sklearn.datasets import load_boston
   from sklearn.metrics import mean_squared_error
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3. Load your dataset. In this example, we'll use the Boston Housing dataset provided by scikit-learn.

   main.py
   boston = load_boston()
   X, y = boston.data, boston.target
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4. Split the dataset into training and testing sets:

   main.py
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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5. Prepare the data for XGBoost:

   main.py
   dtrain = xgb.DMatrix(X_train, label=y_train)
   dtest = xgb.DMatrix(X_test, label=y_test)
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6. Set the hyperparameters for the XGBoost model:

   main.py
   params = {
       'objective': 'reg:squarederror',
       'max_depth': 3,
       'learning_rate': 0.1,
       'subsample': 0.8,
       'colsample_bytree': 0.8,
       'seed': 42
   }
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7. Train the XGBoost model:

   main.py
   num_rounds = 100
   model = xgb.train(params, dtrain, num_rounds)
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8. Make predictions on the test set:

   main.py
   y_pred = model.predict(dtest)
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9. Evaluate the model's performance:

   main.py
   mse = mean_squared_error(y_test, y_pred)
   print(f"Mean Squared Error: {mse}")
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This is a basic example of how to use XGBoost with scikit-learn in Python. You can adjust the hyperparameters and modify the code to fit your specific use case.