XGB Regressor

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from xgboost import XGBClassifier
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+
+
+def main() -> None:
+
+    """
+    The Url for the algorithm
+    https://xgboost.readthedocs.io/en/stable/
+    Iris type dataset is used to demonstrate algorithm.
+    """
+
+    # Load Iris dataset
+    iris = load_iris()
+
+    # Split dataset into train and test data
+    x = iris["data"]  # features
+    y = iris["target"]
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.3, random_state=1
+    )
+
+    # XGBoost Classifier
+    xgb = XGBClassifier()
+    xgb.fit(x_train, y_train)
+
+    # Display Confusion Matrix of Classifier
+    plot_confusion_matrix(
+        xgb,
+        x_test,
+        y_test,
+        display_labels=iris["target_names"],
+        cmap="Blues",
+        normalize="true",
+    )
+    plt.title("Normalized Confusion Matrix - IRIS Dataset")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()

machine_learning/xgboostregressor.py

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+# -*- coding: utf-8 -*-
+"""xgboostregressor.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1UrXXhxQNEI3rL3182GyZFCqPqyhE1c5g
+"""
+
+# XGBoost Regressor Example
+from sklearn.datasets import load_boston
+from xgboost import XGBRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sklearn.model_selection import train_test_split
+
+
+def main():
+
+    """
+    The Url for the algorithm
+    https://xgboost.readthedocs.io/en/stable/
+    Boston house price dataset is used to demonstrate the algorithm.
+    """
+    # Load Boston house price dataset
+    boston = load_boston()
+    print(boston.keys())
+
+    # Split dataset into train and test data
+    x = boston["data"]  # features
+    y = boston["target"]
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.25, random_state=1
+    )
+
+    #XGBoost Regressor
+    xgb=XGBRegressor()
+    xgb.fit(x_train, y_train)
+
+    # Predict target for test data
+    predictions = xgb.predict(x_test)
+    predictions = predictions.reshape(len(predictions), 1)
+
+    # Error printing
+    print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
+    print(f"Mean Square Error  :\t {mean_squared_error(y_test, predictions)}")
+
+
+if __name__ == "__main__":
+    main()