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XGBoost Configure "merror" Eval Metric

When training an XGBoost model for multiclass classification tasks, the Multiclass Classification Error (merror) is a useful evaluation metric to monitor your model’s performance.

merror measures the fraction of incorrect class predictions made by the model during training or validation.

By setting eval_metric='merror', you can track your model’s accuracy and enable early stopping to prevent overfitting.

Here’s an example of how to use merror as the evaluation metric with XGBoost and scikit-learn:

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
import matplotlib.pyplot as plt

# Generate a synthetic multiclass classification dataset
X, y = make_classification(n_samples=1000, n_classes=3, n_informative=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create an XGBClassifier with merror as the evaluation metric
model = XGBClassifier(n_estimators=100, eval_metric='merror', early_stopping_rounds=10, random_state=42)

# Train the model with early stopping
model.fit(X_train, y_train, eval_set=[(X_test, y_test)])

# Retrieve the merror values from the training process
results = model.evals_result()
epochs = len(results['validation_0']['merror'])
x_axis = range(0, epochs)

# Plot the merror values
plt.plot(x_axis, results['validation_0']['merror'], label='Test')
plt.xlabel('Number of Boosting Rounds')
plt.ylabel('Multiclass Classification Error')
plt.title('XGBoost merror Performance')

In this example, we generate a synthetic multiclass classification dataset using scikit-learn’s make_classification function with 3 classes. We then split the data into training and testing sets.

We create an instance of XGBClassifier and set eval_metric='merror' to specify the Multiclass Classification Error as the evaluation metric. We also set early_stopping_rounds=10 to enable early stopping if the merror doesn’t improve for 10 consecutive rounds.

During training, we pass the testing set as the eval_set to monitor the model’s performance on unseen data. After training, we retrieve the merror values using the evals_result() method.

Finally, we plot the merror values against the number of boosting rounds to visualize the model’s performance during training. This plot helps us assess whether the model is overfitting or underfitting and determines the optimal number of boosting rounds.

By using merror as the evaluation metric, we can effectively monitor the model’s multiclass classification performance, prevent overfitting through early stopping, and select the best model based on the lowest merror value.

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