KNN on scaled data
The accuracy score on the unscaled wine dataset was decent, but let's see what you can achieve by using standardization. Once again, the knn model as well as the X and y data and labels set have already been created for you.
Questo esercizio fa parte del corso
Preprocessing for Machine Learning in Python
Istruzioni dell'esercizio
- Create the
StandardScaler()method, stored in a variable namedscaler. - Scale the training and test features, being careful not to introduce data leakage.
- Fit the
knnmodel to the scaled training data. - Evaluate the model's performance by computing the test set accuracy.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
# Instantiate a StandardScaler
scaler = ____
# Scale the training and test features
X_train_scaled = ____.____(____)
X_test_scaled = ____.____(____)
# Fit the k-nearest neighbors model to the training data
____.____(____, ____)
# Score the model on the test data
print(____.____(____, ____))