INFO
A fully connected architecture used for structured data classification and regression.
- Serves as a foundational model for tabular prediction tasks
Components
- Input Layer: Accepts structured features
- Hidden Layers: Learn non-linear transformations
- Output Layer: Produces final prediction
- Dropout Layers: Prevent overfitting
- Activation Functions: Enable complex decision boundaries
Key Features
- Non-Linear Modeling
- Captures complex relationships in data
- High Dimensional Input Handling
- Works well with large feature sets
- General-Purpose Flexibility
- Adaptable to many domains
- Efficient Training
- Optimized via gradient descent and regularization
Business Applications
- Fraud Detection
- Identifies anomalies in insurance claims
- Credit Scoring
- Assesses borrower risk profiles
- Talent Analytics
- Predicts employee performance and attrition
- Loan Approval Optimization
- Improves decision accuracy and profitability