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MulticlassClassifier: Robust Multiclass Classification with Machine Gnostics

The MulticlassClassifier is a robust multiclass classification model built on the Machine Gnostics framework. It provides feature-rich classification with polynomial feature expansion, softmax activation, and detailed history tracking, designed to be resilient to outliers and improve model stability.

Overview

Machine Gnostics MulticlassClassifier brings deterministic, event-level modeling to multiclass problems. By leveraging gnostic algebra and geometry, it provides robust, interpretable, and reproducible results.

  • Deterministic & Finite: No randomness or probability; all computations are reproducible.
  • Event-Level Modeling: Handles uncertainty and error at the level of individual data events.
  • Robust: Designed to be robust against outliers, corrupted data, and distributional shifts.
  • Flexible: Supports polynomial feature expansion and automatic class detection.
  • mlflow Integration: For experiment tracking and deployment.
  • Easy Model Persistence: Save and load models with joblib.

Key Features

  • Multiclass classification using softmax activation
  • Polynomial feature expansion up to a user-specified degree
  • Gnostic weights for robust handling of outliers
  • Automatic detection of number of classes
  • Iterative optimization with early stopping and convergence tolerance
  • Training history tracking for analysis and visualization
  • Compatible with numpy arrays for input/output

Parameters

Parameter Type Default Description
degree int 1 Degree of polynomial features to use for input expansion.
max_iter int 100 Maximum number of iterations for the optimization algorithm.
tolerance float 1e-1 Tolerance for convergence.
early_stopping bool True Whether to stop training early if convergence is detected.
verbose bool False If True, prints detailed logs during training.
scale str | int | float 'auto' Scaling method for gnostic weight calculations.
data_form str 'a' Internal data representation format.
gnostic_characteristics bool False If True, computes and records gnostic characteristics.
history bool True If True, records the optimization history for analysis.

Attributes

  • coefficients: np.ndarray
    • Fitted model coefficients after training.
  • weights: np.ndarray
    • Sample weights used during training.
  • num_classes: int
    • Number of unique classes in the training data.
  • cross_entropy_loss: float
    • Cross-entropy loss computed during training.
  • params: list of dict
    • List of model parameters (for compatibility and inspection).
  • _history: list
    • List of dictionaries containing training history (loss, coefficients, entropy, etc.).
  • degree, max_iter, tolerance, early_stopping, verbose, scale, data_form, gnostic_characteristics
    • Configuration parameters as set at initialization.

Methods

fit(X, y)

Fit the multiclass classifier to the data.

This method trains the classifier using the provided input features and target labels. It supports polynomial feature expansion, softmax activation, and early stopping.

Parameters

  • X: array-like or DataFrame of shape (n_samples, n_features)
    • Input features for training.
  • y: array-like of shape (n_samples,)
    • Target labels for training (class integers starting from 0).

Returns

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

predict(model_input)

Predict class labels for new data.

Parameters

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

Returns

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

predict_proba(model_input)

Predict class probabilities for new data.

Parameters

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

Returns

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

score(X, y)

Compute the accuracy score of the model on given data.

Parameters

  • X: array-like or DataFrame of shape (n_samples, n_features)
    • Input features for evaluation.
  • y: array-like of shape (n_samples,)
    • True class labels.

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 MulticlassClassifier with loaded parameters.

Example Usage

import numpy as np
from machinegnostics.models import MulticlassClassifier

# Generate synthetic multiclass data (3 classes)
np.random.seed(42)
n_samples = 150
X = np.random.randn(n_samples, 2)
# Assign classes based on regions
y = np.zeros(n_samples, dtype=int)
y[X[:, 0] > 0.5] = 1
y[X[:, 1] < -0.5] = 2

# Initialize model
model = MulticlassClassifier(
    degree=2,
    max_iter=100,
    verbose=True,
    tolerance=1e-3
)

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

# Predict
X_test = np.array([[1.0, 1.0], [-1.0, -1.0], [0.0, -1.0]])
y_pred = model.predict(X_test)
y_proba = model.predict_proba(X_test)

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

Multi Class Classification

Training History

If history=True, the model records detailed training history at each iteration, accessible via model.params and model._history. Each entry contains details like cross-entropy loss, coefficients, and entropy.

Notes

  • The model automatically detects the number of classes from the training data.
  • Uses softmax activation for multiclass probability estimation.

Author: Nirmal Parmar