Modeling without normalizing

Let's take a look at what might happen to your model's accuracy if you try to model data without doing some sort of standardization first.

Here we have a subset of the wine dataset. One of the columns, Proline, has an extremely high variance compared to the other columns. This is an example of where a technique like log normalization would come in handy, which you'll learn about in the next section.

The scikit-learn model training process should be familiar to you at this point, so we won't go too in-depth with it. You already have a k-nearest neighbors model available (knn) as well as the X and y sets you need to fit and score on.

This exercise is part of the course

Preprocessing for Machine Learning in Python

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Exercise instructions

Split up the X and y sets into training and test sets, ensuring that class labels are equally distributed in both sets.
Fit the knn model to the training features and labels.
Print the test set accuracy of the knn model using the .score() method.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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

knn = KNeighborsClassifier()

# Fit the knn model to the training data
knn.____(____, ____)

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

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