A data scientist is using grid search for hyperparameter optimisation for kNN. The grid search evaluates 5 values of $k$, 4 different distance functions, and 3 different feature combinations using 5-fold cross-validation. How many total model training operations will be performed during this hyperparameter optimisation process?

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BlackTom AI · Accepted Answer

We start by identifying the components involved in the grid search:
- Number of k values to try: 5
- Number of distance functions: 4
- Number of feature combinations: 3
These three factors multiply to give the total number of hyperparameter configurations tested:
5 × 4 × 3 = 60 configurations.
Next, each configuration is evaluated using 5-fold cross-validation. In 5-fold CV, the model is trained and evaluated five times for each configuration, corresponding to the five folds. Therefore, the total number of model training operations is:
60 configurations × 5 folds = 300 training operations.
Thus, the hyperparameter optimisation process involves 300 total model trainings. If there were multiple options to compare, this would be the figure to match for the total training count. The calculation hinges on multiplying the counts of each grid search dimension (k values, distance functions, feature combinations) by the cross-validation folds.

QBUS6810 (ND) Mid-Semester Practice Quiz

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