---
title: AIO2025M06 DEMO Softmax REGRESSION
emoji: 📊
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.38.0
app_file: app.py
short_description: Run Softmax Regression on datasets to predict outcomes
pinned: false
---

# Softmax Regression Demo

Interactive demonstration of Logistic Regression implemented from scratch using NumPy and gradient descent. Learn binary classification with sigmoid activation, binary cross-entropy loss, and adjustable prediction threshold.

## Features

- **Multi-class Classification**: Implements classification for 3 or more classes (e.g., 0, 1, 2, ...)
- **NumPy Implementation**: Efficient matrix operations for fast computation
- **Softmax Activation**: Maps raw scores (logits) to a probability distribution over multiple classes (summing to 1)
- **Categorical Entropy Loss**: Optimized loss function for multi-class classification
- **Adjustable Threshold**: Experiment with different probability thresholds to balance precision/recall
- **Mini-batch Gradient Descent**: Supports configurable batch sizes (powers of 2) or full batch
- **Feature Normalization**: Automatic standardization (zero mean, unit variance) for stable training
- **Training Visualization**: Track loss and accuracy over epochs for training and validation sets

## Algorithm Details

**Activation Function**: Softmax σ(z) = e^(z_k) / Σ_j e^(z_j) 

**Loss Function**: Categorical Cross-Entropy L = -Σ(k=0 to K-1) y_k · log(ŷ_k)

**Classification**: Predict the class with the highest probability ŷ_k if its probability ≥ threshold, otherwise classify as "Undecided" or the highest probability class regardless of threshold.  

**Normalization**: Features standardized (zero mean, unit variance) for numerical stability

## Sample Datasets

1. **Iris**: Classic multi-class dataset (3 classes of iris flowers)
2. **Wine**: Wine dataset (3 distinct classes)
3. **Synthetic**: Artificially generated multi-class classification dataset

## How to Use

1. **Select Data**: Choose a sample dataset or upload your own CSV/Excel file
2. **Configure Target**: Select target column (must have exactly 2 unique values)
3. **Set Training Parameters**:
   - **Epochs**: Number of training iterations (recommended: 50-500)
   - **Learning Rate**: Step size for gradient descent (recommended: 0.001-0.01)
   - **Batch Size**: Samples per batch (powers of 2, or Full Batch)
   - **Train/Validation Split**: Proportion for training (default: 80%)
4. **Adjust Threshold**: Set probability threshold for classification (default: 0.5)
5. **Enter Features**: Input feature values for prediction
6. **Run Training**: Click "Run Training & Prediction" to train and visualize

## Key Parameters

**Training Parameters**:
- **Epochs**: Complete passes through data. More epochs = better learning but risk of overfitting
- **Learning Rate**: Step size (0.001-0.01 recommended). Too high causes instability, too low is slow
- **Batch Size**: Samples processed before update. Smaller = faster but noisier, larger = more stable
- **Train/Validation Split**: Data split ratio (default 80/20)

**Threshold Parameter** (Key Feature):
- **Default**: 0.5 (balanced classification)
- **Lower threshold** (e.g., 0.3): More class 1 predictions → higher recall, lower precision
- **Higher threshold** (e.g., 0.7): Fewer class 1 predictions → higher precision, lower recall
- **Experiment**: Adjust threshold to see how predictions and accuracy change in real-time
- **Use Case**: Balance precision vs recall based on your classification goals

## Requirements

- gradio >= 5.38.0
- pandas >= 1.5.0
- scikit-learn >= 1.3.0
- numpy >= 1.24.0
- plotly >= 5.15.0