Machine Learning for OpenCV 4 - Second Edition

Implementing a simple grid search

Returning to our kNN classifier, we find that we have only one hyperparameter to tune: k. Typically, you would have a much larger number of open parameters to mess with, but the kNN algorithm is simple enough for us to manually implement a grid search.

Before we get started, we need to split the dataset as we have done before into training and test sets:

1. Here we choose a 75-25 split:

In [1]: from sklearn.datasets import load_iris...     import numpy as np...     iris = load_iris()...     X = iris.data.astype(np.float32)...     y = iris.targetIn [2]: X_train, X_test, y_train, y_test = train_test_split(...          X, y, random_state=37...      )

2. Then, the goal is to loop over all possible values of k. As we do this, we want to keep ...