ML Design Pattern——Hyperparameter Tuning

Hyperparameter tuning is the process of finding the optimal set of hyperparameters for a machine learning model. Hyperparameters are settings that control the learning process, but aren't learned from the model's training data itself. They govern aspects like model complexity, how quickly it learns, and how sensitive it is to outliers.

Key concepts:

• Hyperparameters: Settings like learning rate, number of neurons in a neural network, or tree depth in a decision tree.
• Model performance: Measured using metrics like accuracy, precision, recall, or F1-score on a validation set (not part of the training data).
• Search space: The range of possible hyperparameter values.
• Search strategy: The method used to explore the search space (e.g., grid search, random search, Bayesian optimization).

Visualizing hyperparameter tuning:

Common hyperparameter tuning techniques:

• Grid search: Exhaustively evaluates every combination of hyperparameters within a specified grid.
• Random search: Randomly samples combinations of hyperparameters from the search space.
• Bayesian optimization: Uses a probabilistic model to guide the search, focusing on more promising areas.

Importance of hyperparameter tuning:

• Significantly impacts model performance
• Ensures model generalizes well to unseen data
• Can be computationally expensive, but often worth the effort

Additional considerations:

• Early stopping: Monitor validation performance during training and stop when it starts to degrade, preventing overfitting.
• Regularization: Techniques to reduce model complexity and prevent overfitting, often controlled by hyperparameters.

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Breakpoints in Decision Trees:

• Definition: Breakpoints are the specific values of a feature that partition the data into different branches of a decision tree.
• Function: They determine the decision-making rules at each node of the tree.
• Visualization: Imagine a tree with branches for different outcomes based on feature values (e.g., "age > 30" leads to one branch, "age <= 30" to another).
• Key points:
  • Chosen to maximize information gain or purity in each branch.
  • Location significantly impacts model complexity and accuracy.

Weights in Neural Networks:

• Definition: Numerical values associated with connections between neurons, representing the strength and importance of each connection.
• Function: Determine how much influence one neuron's output has on another's activation.
• Visualization: Picture a network of interconnected nodes with varying strengths of connections (like thicker or thinner wires).
• Key points:
  • Learned during training to minimize error and optimize model performance.
  • Encoded knowledge of the model, capturing patterns in the data.
  • Adjusting weights is the core of neural network learning.

Support Vectors in SVMs:

• Definition: Data points that lie closest to the decision boundary in SVMs, crucial for defining the margin that separates classes.
• Function: Determine the optimal hyperplane that best separates classes in high-dimensional space.
• Visualization: Imagine points near a dividing line acting as "fence posts" to define the boundary.
• Key points:
  • Only a small subset of training data points become support vectors, making SVMs memory efficient.
  • Removing non-support vectors doesn't affect the decision boundary.
  • Highly influential in model predictions.

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KerasTuner

KerasTuner is a library that automates hyperparameter tuning for Keras models, making it easier to find optimal configurations.

https://www.analyticsvidhya.com/blog/2021/08/easy-hyperparameter-tuning-in-neural-networks-using-keras-tuner/