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Hyperparameter Tuning

Updated May 15, 2023 ·

Hyperparameters​

Hyperparameters are values set before training that contorl the learning process.

  • Hyperparameters must be chosen carefully
  • They significantly influence the model's accuracy.
  • Cannot be learned from data during training

Model parameters, like weights and biases, are learned during training. Hyperparameters, on the other hand, must be set before training begins. Examples include:

  • Model Architecture in a Neural Networ;
  • Number of branches in a decision tree
  • Learning rate

Hyperparameter Tuning​

Hyperparameter tuning is used to improve the performance of machine learning models.

  • Adjusting hyperparameters to improve model performance
  • Focus on factors like learning rate, number of layers, or tree depth
  • Common starting point for model optimization

Tuning helps find the best combination of hyperparameters for the model.

Hyperparameter Tuning Methods​

Different methods offer distinct approaches to hyperparameter tuning.

  • Grid Search

    • Tests all combinations from a predefined grid
    • Exhaustive but time-consuming

  • Random Search

    • Randomly tests hyperparameter combinations
    • Faster than grid search but can miss optimal solutions

  • Bayesian Search

    • Uses Bayesian optimization to refine hyperparameter selection
    • Efficient as it learns from past trials to guide future searches

Automated Hyperparameter Tuning​

Manually finding the best hyperparameter combination is time-consuming. Automating this process helps find the best set of hyperparameters efficiently.

  • Saves time and effort
  • Leads to better-performing models
  • Ensures optimal hyperparameter selection

Steps for Automated Tuning​

These steps guide the automated tuning process to find the best model parameters.

  1. Define hyperparameters to tune
  2. Set search space (discrete values or ranges)
  3. Choose a performance metric (e.g., recall, precision)
  4. Set stopping criteria (e.g., maximum trials)

Environment Symmetry​

Consistent settings across environments lead to reliable model performance.

  • Ensures model behaves consistently across environments
  • Prevents unexpected behavior when deploying models

Tracking Experiments​

Automated tracking logs all tuning details to help manage experiments.

  • Automate tracking of hyperparameter experiments
  • Record all hyperparameter combinations explored
  • Store metadata for later reference

Hyperparameter Visualization​

Visualization helps in interpreting the effects of hyperparameter changes.

  • Understand the impacts of different hyperparameters
  • Helps analyze performance variations with different values