KNN on non-scaled data

Before adding standardization to your scikit-learn workflow, you'll first take a look at the accuracy of a K-nearest neighbors model on the wine dataset without standardizing the data.

The knn model as well as the X and y data and labels sets have been created already.

Cet exercice fait partie du cours

Preprocessing for Machine Learning in Python

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Instructions

Split the dataset into training and test sets.
Fit the knn model to the training data.
Print out the test set accuracy of your trained knn model.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Split the dataset and labels into training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, stratify=y, random_state=42)

# Fit the k-nearest neighbors model to the training data
____

# Score the model on the test data
print(____.____(____, ____))

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