XGBoost Classifier

machine_learning/xgboostclassifier.py

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+# XGBoost Classifier Example
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+
+
+def main():
+
+    """
+    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
+    #with both train and test sets
+    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()
+
+    return None
+
+
+if __name__ == "__main__":
+    main()