TDistributionClassifier 1.1.7

A binary classifier using Student's t-distribution for univariate and multivariate continuous data.

TDistributionClassifier

Author: Abdul Mofique Siddiqui
License: MIT
Install via pip:

pip install tdistributionclassifier

Import it in your Python code:

from TDistributionClassifier import TDistributionClassifier

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Overview

TDistributionClassifier is a binary classifier for continuous 1D or multi-dimensional data. It models each class using the Student's t-distribution, making it robust to outliers and suitable for both univariate and multivariate data.

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🔧 Installation

Install the package via pip:

pip install tdistributionclassifier

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How It Works

• Univariate Mode: For 1D features, each class is modeled using a univariate t-distribution.
• Multivariate Mode: For multi-dimensional features, each class is modeled using a multivariate t-distribution.
• Uses log-probabilities and the log-sum-exp trick for numerical stability.
• Automatically detects the input dimensionality and selects the appropriate mode.

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Getting Started

1. Import the package

from TDistributionClassifier import TDistributionClassifier

2. Initialize the classifier

clf = TDistributionClassifier()

3. Fit the model

clf.fit(X_train, y_train)

• X_train: numpy array of shape (n_samples,) or (n_samples, n_features)
• y_train: binary labels (0 or 1)

4. Predict class probabilities

probs = clf.predict_proba(X_test)

• Returns a numpy array of shape (n_samples, 2) with class 0 and class 1 probabilities.

5. Predict class labels

labels = clf.predict(X_test)

• Returns predicted class labels (0 or 1)

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API Reference

TDistributionClassifier()

Initializes the classifier. No arguments required.

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.fit(X, y)

Fits the model to the training data.

• Parameters:
  • X: numpy array of training features. Shape: (n_samples,) or (n_samples, n_features)
  • y: binary class labels (0 or 1). Shape: (n_samples,)

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.predict_proba(X)

Returns predicted class probabilities.

• Input:
  • X: Features. Shape: (n_samples,) or (n_samples, n_features)
• Output:
  • probs: array of shape (n_samples, 2) with [P(class=0), P(class=1)]

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.predict(X)

Returns predicted class labels based on highest probability.

• Input:
  • X: Features. Shape: (n_samples,) or (n_samples, n_features)
• Output:
  • labels: array of shape (n_samples,), values are 0 or 1

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Example Usage

from TDistributionClassifier import TDistributionClassifier
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# Load data and binarize target
data = load_diabetes()
X = data.data
y = (data.target > 100).astype(int)  # Binary classification

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Initialize and train
clf = TDistributionClassifier()
clf.fit(X_train, y_train)

# Predict
probs = clf.predict_proba(X_test)
preds = clf.predict(X_test)

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Internals

• PDF Estimation: Uses scipy.stats.t (univariate) or scipy.stats.multivariate_t (multivariate).
• Regularization: Adds small noise (1e-6 * I) to covariance matrices to ensure invertibility.
• Numerical Stability: Log-probabilities with log-sum-exp used for probability normalization.

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Notes

• Only supports binary classification (0 and 1).
• Multivariate mode is triggered when input has >1 features.
• If data is not linearly separable, consider applying feature transformation or dimensionality reduction before use.

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Author

Abdul Mofique Siddiqui

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License

This project is licensed under the MIT License.