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GnosticRandomForestClassifier: Robust Random Forest with Machine Gnostics

The GnosticRandomForestClassifier extends the standard random forest approach by integrating Mathematical Gnostics principles. It employs an iterative reweighting scheme that assesses the quality of each data sample based on the consistency of the previous iteration's model predictions. This allows the forest to autonomously down-weight outliers and noise (mislabeled samples), resulting in a more robust predictive model.

Overview

Machine Gnostics GnosticRandomForestClassifier is designed for robust classification in the presence of label noise and outliers. By combining the ensemble power of Random Forests with the robust weighting of Machine Gnostics, it offers superior stability and accuracy in challenging data environments.

  • Robustness to Label Noise: Automatically identifies and down-weights samples with low classification confidence.
  • Iterative Refinement: Optimizes sample weights over multiple iterations until convergence.
  • Ensemble Learning: Leverages multiple decision trees for reduced variance and improved generalization.
  • 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
  • Iterative refinement of sample weights
  • Standard Random Forest hyperparameters (n_estimators, max_depth, etc.)
  • Identifies and handles outliers/mislabeled data automatically
  • Convergence-based early stopping
  • Training history tracking for analysis
  • Compatible with NumPy arrays and Pandas DataFrame/Series for input/output

Parameters

Parameter Type Default Description
n_estimators int 100 The number of trees in the forest.
max_depth int None Maximum depth of the tree.
min_samples_split int 2 Minimum samples to split a node.
gnostic_weights bool True Whether to use iterative gnostic weights.
max_iter int 10 Maximum gnostic iterations.
tolerance float 1e-4 Convergence tolerance.
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.

Attributes

  • weights: np.ndarray
    • The final calibrated sample weights assigned to the training data.
  • trees: list
    • The list of underlying decision trees (estimators) that make up the forest.
  • classes_: np.ndarray
    • The classes labels.
  • _history: list
    • List of dictionaries containing training history (loss, entropy, weights).
  • n_estimators, gnostic_weights, max_depth, max_iter, tolerance
    • Configuration parameters as set at initialization.

Methods

fit(X, y)

Fit the Gnostic Forest model to the training data.

This method trains the random forest classifier. If gnostic_weights is True, it iteratively refines the model by reweighting samples based on gnostic residuals to down-weight outliers.

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: GnosticRandomForestClassifier
    • 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.

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 GnosticRandomForestClassifier with loaded parameters.

Example Usage

from machinegnostics.models import GnosticRandomForestClassifier

# Initialize model
model = GnosticRandomForestClassifier(
    n_estimators=50,
    gnostic_weights=True,
    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 Random Forest Cls

Training History

If history=True, the model records detailed training history at each iteration, accessible via model._history. This helps in analyzing how the model identifies and down-weights noisy samples over time.

Notes

  • This model is particularly effective when the training labels contain noise or errors.
  • The gnostic_weights mechanism allows the forest to "self-clean" the training data during the fitting process.

Author: Nirmal Parmar
Date: 2026