doc: architecture

Architecture_Recommendations.md

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+# Neural Network Architecture Recommendations for Loan Prediction
+
+## Dataset Characteristics (Key Factors for Architecture Design)
+
+- **Input Features**: 9 carefully selected numerical features
+- **Training Samples**: 316,824 (large dataset)
+- **Test Samples**: 79,206
+- **Problem Type**: Binary classification
+- **Class Distribution**: 80.4% Fully Paid, 19.6% Charged Off (moderate imbalance)
+- **Feature Correlations**: Low to moderate (max 0.632)
+- **Data Quality**: Clean, standardized, no missing values
+
+## Recommended Architecture: Moderate Deep Network
+
+### Architecture Overview
+
+```
+Input Layer (9 neurons) 
+    ↓
+Hidden Layer 1 (64 neurons, ReLU)
+    ↓
+Dropout (0.3)
+    ↓
+Hidden Layer 2 (32 neurons, ReLU)
+    ↓
+Dropout (0.2)
+    ↓
+Hidden Layer 3 (16 neurons, ReLU)
+    ↓
+Dropout (0.1)
+    ↓
+Output Layer (1 neuron, Sigmoid)
+```
+
+## Detailed Architecture Justification
+
+### 1. Network Depth: 3 Hidden Layers
+**Why this choice:**
+- **Sufficient complexity**: Financial relationships often involve non-linear interactions
+- **Large dataset**: 316k samples can support deeper networks without overfitting
+- **Not too deep**: Avoids vanishing gradient problems with tabular data
+- **Sweet spot**: Balances complexity with training stability
+
+### 2. Layer Sizes: [64, 32, 16]
+**Rationale:**
+- **Funnel architecture**: Progressively reduces dimensionality (9→64→32→16→1)
+- **Power of 2 sizes**: Computationally efficient, standard practice
+- **64 first layer**: 7x input size allows good feature expansion
+- **Progressive reduction**: Enables hierarchical feature learning
+- **16 final layer**: Sufficient bottleneck before final decision
+
+### 3. Activation Functions
+**ReLU for Hidden Layers:**
+- **Computational efficiency**: Faster than sigmoid/tanh
+- **Avoids vanishing gradients**: Critical for deeper networks
+- **Sparsity**: Creates sparse representations
+- **Standard choice**: Proven effective for tabular data
+
+**Sigmoid for Output:**
+- **Binary classification**: Perfect for probability output [0,1]
+- **Smooth gradients**: Better than step function
+- **Interpretable**: Direct probability interpretation
+
+### 4. Dropout Strategy: [0.3, 0.2, 0.1]
+**Progressive dropout rates:**
+- **Higher early dropout (0.3)**: Prevents early layer overfitting
+- **Reducing rates**: Allows final layers to learn refined patterns
+- **Conservative final dropout**: Preserves important final representations
+- **Prevents overfitting**: Critical with large dataset
+
+### 5. Regularization Considerations
+**Additional techniques to consider:**
+- **L2 regularization**: Weight decay of 1e-4 to 1e-5
+- **Batch normalization**: For training stability (optional)
+- **Early stopping**: Monitor validation loss
+
+## Alternative Architectures
+
+### Option 1: Lighter Network (Faster Training)
+```
+Input (9) → Dense(32, ReLU) → Dropout(0.2) → Dense(16, ReLU) → Dropout(0.1) → Output(1, Sigmoid)
+```
+**When to use:** If training time is critical or simpler patterns suffice
+
+### Option 2: Deeper Network (Maximum Performance)
+```
+Input (9) → Dense(128, ReLU) → Dropout(0.3) → Dense(64, ReLU) → Dropout(0.3) → 
+Dense(32, ReLU) → Dropout(0.2) → Dense(16, ReLU) → Dropout(0.1) → Output(1, Sigmoid)
+```
+**When to use:** If computational resources are abundant and maximum accuracy is needed
+
+### Option 3: Wide Network (Feature Interactions)
+```
+Input (9) → Dense(128, ReLU) → Dropout(0.3) → Dense(128, ReLU) → Dropout(0.2) → 
+Dense(64, ReLU) → Dropout(0.1) → Output(1, Sigmoid)
+```
+**When to use:** To capture more complex feature interactions
+
+## Training Hyperparameters
+
+### Learning Rate Strategy
+- **Initial rate**: 0.001 (Adam optimizer default)
+- **Schedule**: ReduceLROnPlateau (factor=0.5, patience=10)
+- **Minimum rate**: 1e-6
+
+### Batch Size
+- **Recommended**: 512 or 1024
+- **Rationale**: Large dataset allows bigger batches for stable gradients
+- **Memory consideration**: Adjust based on GPU/CPU capacity
+
+### Optimizer
+- **Adam**: Best for most scenarios
+- **Alternative**: AdamW with weight decay
+- **Why Adam**: Adaptive learning rates, momentum, proven with neural networks
+
+### Loss Function
+- **Binary Cross-Entropy**: Standard for binary classification
+- **Class weights**: Consider class_weight='balanced' due to 80/20 split
+- **Alternative**: Focal loss if class imbalance becomes problematic
+
+### Training Strategy
+- **Epochs**: Start with 100, use early stopping
+- **Validation split**: 20% of training data
+- **Early stopping**: Patience of 15-20 epochs
+- **Metrics**: Track accuracy, precision, recall, AUC-ROC
+
+## Why This Architecture is Optimal
+
+### 1. **Matches Data Complexity**
+- 9 features suggest moderate complexity needs
+- Network size proportional to feature count
+- Sufficient depth for non-linear patterns
+
+### 2. **Handles Class Imbalance**
+- Dropout prevents majority class overfitting
+- Multiple layers allow nuanced decision boundaries
+- Sufficient capacity for minority class patterns
+
+### 3. **Computational Efficiency**
+- Not overly complex for the problem
+- Reasonable training time
+- Good inference speed
+
+### 4. **Generalization Ability**
+- Progressive dropout prevents overfitting
+- Balanced depth/width ratio
+- Suitable regularization
+
+### 5. **Financial Domain Appropriate**
+- Conservative architecture (financial decisions need reliability)
+- Interpretable through feature importance analysis
+- Robust to noise in financial data
+
+## Expected Performance
+
+### Baseline Expectations
+- **Accuracy**: 82-85% (better than 80% baseline)
+- **AUC-ROC**: 0.65-0.75 (good discrimination)
+- **Precision**: 85-90% (low false positives important)
+- **Recall**: 75-85% (catch most defaults)
+
+### Performance Monitoring
+- **Validation curves**: Should show convergence without overfitting
+- **Learning curves**: Should indicate sufficient training data
+- **Confusion matrix**: Should show balanced performance across classes
+
+## Implementation Recommendations
+
+### 1. Start Simple
+- Begin with recommended architecture
+- Establish baseline performance
+- Iteratively increase complexity if needed
+
+### 2. Systematic Tuning
+- First optimize architecture (layers, neurons)
+- Then tune training hyperparameters
+- Finally adjust regularization
+
+### 3. Cross-Validation
+- Use stratified k-fold (k=5) for robust evaluation
+- Ensures consistent performance across different data splits
+
+### 4. Feature Importance
+- Analyze trained network feature importance
+- Validates feature selection from EDA
+- Identifies potential for further feature engineering
+
+This architecture provides an excellent balance of complexity, performance, and reliability for your loan prediction problem.