INFO
A supervised learning model designed to predict continuous numerical values using a feedforward neural network architecture.
- Ideal for tasks like price prediction, demand forecasting, and risk scoring
- Learns complex, non-linear relationships between input features and target outputs
Components
- Input Layer
- Accepts structured features (e.g., numerical, categorical embeddings)
- Hidden Layers
- Perform non-linear transformations using activation functions (e.g., ReLU, tanh)
- Output Layer
- Typically a single neuron with linear activation for continuous output
- Loss Function
- Common choices: Mean Squared Error (MSE), Mean Absolute Error (MAE)
- **Optimizer
- Gradient-based methods like Adam, SGD for weight updates
- Regularization
- Techniques like dropout or L2 penalty to prevent overfitting
Key Features
- Continuous Output Prediction
- Unlike classification, outputs are real-valued (e.g., prices, temperatures)
- Non-Linear Modeling
- Captures complex relationships beyond linear regression
- Flexible Architecture
- Depth and width can be tuned for task complexity
- End-to-End Learning
- Learns directly from raw or engineered features to output
- Scalable to High-Dimensional Data
- Handles large feature sets with appropriate regularization
Business Applications
- Real Estate
- Predict housing prices from features like size, location, amenities
- Finance
- Forecast stock prices, credit risk scores, or loan default probabilities
- Retail
- Estimate future demand or sales volume based on historical data
- Energy
- Predict consumption patterns for load balancing and cost optimization
- Healthcare
- Model patient outcomes or treatment costs from clinical data