Evaluating KNN performance

Evaluating the performance of KNN models is essential for understanding how well the model makes predictions and where it may need improvement. This recipe will cover various techniques for assessing KNN performance, including confusion matrices, precision, recall, and F1-scores.

Getting ready

Again, we will use our toy dataset from before, the Iris dataset, and import a few new functions to help us evaluate our model. We’ll evaluate our model using cross-validation scores, learning curves, and a confusion matrix.

Load the libraries:

from sklearn.model_selection import learning_curve
from sklearn.metrics import confusion_matrix
import seaborn as sns

Next, we’ll train our model using the hyperparameters that we found performed the best.

How to do it…

To evaluate the performance of the KNN model, we’ll use three different approaches:

1. Get cross-validation scores. These will be from the model trained...