Machine Learning
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Random ForestAlgorithm
Random Forest
Input → Output
Input: Features (X) + Labels (y)
Output: Predictions + Feature Importance
Core Process
- Build multiple decision trees
- Each tree votes on prediction
- Majority vote = final prediction
- Average feature importance across trees
Key Parameters
n_estimators: 100-500 max_depth: 10-20 min_samples_split: 2-10
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Neural NetworksAlgorithm
Neural Networks
Input → Output
Input: Features (X) + Labels (y)
Output: Predictions + Learned Representations
Core Process
- Forward pass through layers
- Compute loss function
- Backpropagate gradients
- Update weights
Key Parameters
layers: [input, hidden, output] activation: relu/sigmoid/tanh learning_rate: 0.001-0.1
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Support Vector MachineAlgorithm
Support Vector Machine
Input → Output
Input: Features (X) + Labels (y)
Output: Predictions + Support Vectors
Core Process
- Find optimal hyperplane
- Maximize margin between classes
- Use support vectors for boundary
- Apply kernel transformation
Key Parameters
kernel: linear/rbf/poly C: 0.1-100 gamma: 0.001-1.0
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OptunaTool
Optuna
Input → Output
Input: Parameter space + Objective function
Output: Optimized parameters + Best score
Core Process
- Define parameter search space
- Create objective function
- Run optimization trials
- Return best parameters
Key Parameters
n_trials: 100-1000 direction: minimize/maximize sampler: tpe/grid/random