Softmax Regression Demo

AIO2025: Module 06.

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About this Softmax Regression Demo

Interactive demonstration of Softmax Regression for multi-class classification. Learn how it uses the Softmax activation function and Categorical Cross-Entropy loss to predict probabilities across multiple categories.

📊 How to Use: Select multi-class data → Configure target → Set training parameters → Enter feature values → Run training!

Start with sample datasets or upload your own CSV/Excel files.

📁 Upload Your Data

🗂️ Sample Datasets

🎯 Target Column

🔄 Loading sample data...

📋 Data Preview (First 5 Rows)

📊 Softmax Regression Parameters

Number of Epochs

Number of training iterations

Learning Rate (Power of 10)

0=1e-6, 1=1e-5, 2=1e-4, 3=1e-3, 4=1e-2, 5=1e-1, 6=1

0 6

Current Learning Rate: 0.01

Batch Size (Power of 2)

Slide to select: 0=1, 1=2, 2=4, 3=8, ... Max=Full Batch

0 10

Current Batch Size: Full Batch

📊 Data Split Configuration

Train/Validation Split Ratio

Proportion of data used for training (e.g., 0.8 = 80% train, 20% validation)

0.6 0.9

📊 Softmax Regression Results & Visualization

Training Loss & Accuracy Over Epochs

Validation Loss & Accuracy Over Epochs

**📊 Softmax Regression Results**

Training details will appear here showing model performance, learned parameters, and predictions with current threshold.

📊 Softmax Regression Guide:

📈 Training Metrics:

Categorical Cross-Entropy (CCE): The loss function used to optimize multi-class models.
Accuracy: Classification accuracy improves during training. Monitor both training and validation accuracy.

🔧 Training Parameters:

Epochs: Number of complete passes through training data. More epochs = better learning, but watch for overfitting.
Learning Rate: Step size for gradient descent. Recommended: 0.001 to 0.01. Too high may cause instability.
Batch Size: Samples processed before updating parameters. Powers of 2: 1, 2, 4, 8... or Full Batch. Smaller = faster updates but noisier. Larger = more stable.
Train/Validation Split: Proportion of data for training vs validation. Default 80/20 split.

🧮 Algorithm Details:

Softmax Activation: Converts raw scores (logits) into a probability distribution that sums to 1.0 across all classes.
Categorical Cross-Entropy (CCE): The loss function used to optimize multi-class models.
Feature Normalization: Automatic standardization (zero mean, unit variance) for stable training.

💡 Tips:

Start with default parameters (100 epochs, learning rate 0.01)
Monitor validation metrics to detect overfitting
Use batch size = Full Batch for most stable training