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.
Diese Übung ist Teil des Kurses
Preprocessing for Machine Learning in Python
Anleitung zur Übung
- Split the dataset into training and test sets.
- Fit the
knnmodel to the training data. - Print out the test set accuracy of your trained
knnmodel.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# 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(____.____(____, ____))