04. Demo 1: Hyperparameter Tuning with Grid Search

Cd13652 C6 L3 Demo 1 V2

In machine learning, finding the optimal combination of hyperparameters significantly improves model performance. This guide details using grid search to automate this process.

Key Steps:

  1. Data Preparation

    • Generate a synthetic dataset with 10,000 samples, 20 features, and 2 classes for classification.
    • Ensure data consistency by checking feature scales.
    • Split data into training and testing sets, using a 70/30 split, vital for validation.

  2. Initial Model Training

    • Use Support Vector Classifier (SVC) with default hyperparameters.
    • Evaluate model accuracy: achieve ~73% cross-validation and test scores.

  3. Grid Search Implementation

    • Import GridSearchCV to automate hyperparameter tuning.
    • Define a search grid for hyperparameters, C (regularization) and Kernel (function type).
    • Conduct the search with multiple cores for faster processing.

  4. Analyzing Results

    • Identify best hyperparameter combo (C=10, Kernel=rbf) achieving ~73.77% accuracy.
    • Display rankings and inspect learning curves.

  5. Further Optimization

    • Drop ineffective parameters and explore other hyperparameters for improvement.

This approach systematically enhances model performance by efficiently exploring hyperparameter space. Additional adjustments can fine-tune results further.

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