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GnosticBoostingClassifier: Robust Gradient Boosting with Machine Gnostics

The GnosticBoostingClassifier integrates the power of Gradient Boosting (via XGBoost) with the robustness of Mathematical Gnostics. It employs an iterative reweighting mechanism that uses gnostic loss functions to assess data quality, allowing the model to autonomously down-weight outliers and noisy samples during training.

Overview

Machine Gnostics GnosticBoostingClassifier combines state-of-the-art gradient boosting with rigorous error handling. By iteratively refining sample weights based on gnostic criteria (like rentropy and information loss), it achieves superior stability and accuracy in datasets with label noise or outliers.

  • Robust Boosting: Upgrades standard XGBoost with gnostic error modeling.
  • Iterative Refinement: Optimizes sample weights over multiple iterations to minimize gnostic loss.
  • Data Quality Handling: Automatically identifies and down-weights low-fidelity or mislabeled samples.
  • Configurable: Supports standard boosting parameters along with gnostic settings.
  • Event-Level Modeling: Handles uncertainty at the level of individual data events.
  • Easy Model Persistence: Save and load models with joblib.

Key Features

  • Robust classification using iterative gnostic reweighting
  • Integration with XGBoost for high-performance gradient boosting
  • Customizable gnostic loss functions ('hi', etc.)
  • Convergence-based early stopping
  • Training history tracking for detailed analysis
  • Compatible with NumPy arrays and Pandas DataFrame/Series for input/output

Parameters

Parameter Type Default Description
n_estimators int 100 Number of boosting rounds.
max_depth int 6 Maximum tree depth for base learners.
learning_rate float 0.3 Boosting learning rate.
max_iter int 10 Maximum number of gnostic reweighting iterations.
tolerance float 1e-4 Convergence tolerance for early stopping.
mg_loss str 'hi' Gnostic loss function to use.
data_form str 'a' Data form: 'a' (additive) or 'm' (multiplicative).
verbose bool False Verbosity.
random_state int None Random seed.
history bool True Whether to record training history.
scale str | float 'auto' Scaling method for input features.
early_stopping bool True Whether to stop training early if convergence is detected.
gnostic_characteristics bool False Whether to compute extended gnostic metrics.

Attributes

  • model: Any
    • The underlying XGBoost classifier instance.
  • weights: np.ndarray
    • The final calibrated sample weights.
  • classes_: np.ndarray
    • Class labels.
  • _history: list
    • List of dictionaries containing training history (loss, entropy, weights).
  • n_estimators, max_depth, learning_rate, max_iter, tolerance
    • Configuration parameters as set at initialization.

Methods

fit(X, y)

Fit the Gnostic Boosting model to the training data.

This method trains the model using an iterative process. In each iteration, an XGBoost classifier is trained, predictions are made, and sample weights are updated based on the gnostic loss of the residuals. This process repeats until convergence or max_iter.

Parameters

  • X: array-like or DataFrame of shape (n_samples, n_features)
    • Input features. Accepts NumPy arrays or Pandas DataFrame.
  • y: array-like or Series of shape (n_samples,)
    • Target labels. Accepts NumPy arrays or Pandas Series/DataFrame column.

Returns

  • self: GnosticBoostingClassifier
    • Returns the fitted model instance for chaining.

predict(model_input)

Predict class labels for input samples.

Parameters

  • model_input: array-like or DataFrame of shape (n_samples, n_features)
    • Input data for prediction. Accepts NumPy arrays or Pandas DataFrame.

Returns

  • y_pred: np.ndarray of shape (n_samples,)
    • Predicted class labels.

predict_proba(model_input)

Predict class probabilities for input samples.

Parameters

  • model_input: array-like or DataFrame of shape (n_samples, n_features)
    • Input data for prediction. Accepts NumPy arrays or Pandas DataFrame.

Returns

  • y_proba: np.ndarray of shape (n_samples, n_classes)
    • Predicted class probabilities.

score(X, y)

Return the mean accuracy on the given test data and labels.

Parameters

  • X: array-like or DataFrame of shape (n_samples, n_features)
    • Input features for evaluation. Accepts NumPy arrays or Pandas DataFrame.
  • y: array-like or Series of shape (n_samples,)
    • True class labels. Accepts NumPy arrays or Pandas Series/DataFrame column.

Returns

  • score: float
    • Accuracy score of the model predictions.

save(path)

Saves the trained model to disk using joblib.

  • path: str Directory path to save the model.

load(path)

Loads a previously saved model from disk.

  • path: str Directory path where the model is saved.

Returns

Instance of GnosticBoostingClassifier with loaded parameters.

Example Usage

from machinegnostics.models import GnosticBoostingClassifier

# Initialize model
model = GnosticBoostingClassifier(
    n_estimators=50,
    learning_rate=0.1,
    max_iter=5,
    verbose=True
)

# Fit the model
model.fit(X, y)

# Predict
preds = model.predict(X[:5])
print("Predictions:", preds)

# Score
acc = model.score(X, y)
print(f'Accuracy: {acc:.4f}')

Gnostic Boosting Cls

Training History

If history=True, the model records detailed training history at each iteration, accessible via model._history. This includes metrics like loss, residual entropy, and weight statistics, allowing users to visualize how the model adapts to the data quality over time.

Notes

  • XGBoost Requirement: This model requires xgboost to be installed in the environment.
  • Robustness: The iterative gnostic weighting makes this model particularly robust against training data with label noise.

Author: Machine Gnostics Team
Date: 2026