XGBoostModelTrainer¶

The XGBoostModelTrainer class is a subclass of the BaseModelTrainer class. It provides functionality for training and predicting using XGBoost models. XGBoost is an optimized gradient boosting library that is popularly used for machine learning tasks.

Configuration¶

XGBoostModelTrainer has no additional configuration beyond what is required in BaseModelTrainer

Methods¶

fit¶

This method trains the XGBoost model on the given data.

def fit(self, data, *args, **kwargs)

Arguments:

data (dict): Dictionary containing input features X_train, y_train, X_valid, and X_test.

Returns:

model: XGBoost model object.

predict¶

This method performs prediction using the trained XGBoost model.

def predict(self, data, *args, **kwargs)

Arguments:

data (dict): Dictionary containing input features X_train, y_train, X_valid, and X_test.
*args, **kwargs: Additional arguments and keyword arguments.

Returns:

dict: Dictionary containing train, valid, and test predictions and probabilities if applicable.

predict_df¶

This method performs prediction using the trained XGBoost model on a pandas DataFrame.

def predict_df(self, df, *args, **kwargs)

Arguments:

df (pandas DataFrame): DataFrame containing input features.

Returns:

numpy array: Predicted output.

predict_proba_df¶

This method performs prediction probabilities estimation of the trained XGBoost model on a pandas DataFrame.

predict_proba_df(self, df, to_list=False, *args, **kwargs)

Arguments:

df (pandas DataFrame): DataFrame containing input features.
to_list (bool): Whether to convert the probabilities into a list.

Returns:

numpy array or list: Predicted probabilities.

Usage¶

from lolpop.component import XGBoostModelTrainer 

... # create datasets

data = {
    "X_train": X_train,
    "y_train": y_train,
    "X_valid": X_valid,
    "X_test": X_test
}

trainer = XGBoostModelTrainer(problem_type="classification", params={"n_estimators": 100})

# Train the XGBoost model
trained_model = trainer.fit(data)

# Perform prediction
predictions = trainer.predict(data)

In the example above, an instance of the XGBoostModelTrainer class is created with a problem type of "classification" and the number of estimators set to 100. The fit method is then used to train the model on the given data. Finally, the predict method is used to make predictions based on the trained model.