Hyperparameters of KNN
To apply the concepts learned in the prior exercise, it is good practice to try out learnings on a new algorithm. The k-nearest-neighbors algorithm is not as popular as it used to be but can still be an excellent choice for data that has groups of data that behave similarly. Could this be the case for our credit card users?
In this case you will try out several different values for one of the core hyperparameters for the knn algorithm and compare performance.
You will have available:
X_train,X_test,y_train,y_testDataFrames
This exercise is part of the course
Hyperparameter Tuning in Python
Exercise instructions
- Build a knn estimator for the following values of
n_neighbors[5,10,20]. - Fit each to the training data and produce predictions.
- Get an accuracy score for each model and print them out.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Build a knn estimator for each value of n_neighbours
knn_5 = KNeighborsClassifier(n_neighbors=____)
knn_10 = KNeighborsClassifier(n_neighbors=____)
knn_20 = KNeighborsClassifier(n_neighbors=____)
# Fit each to the training data & produce predictions
knn_5_predictions = knn_5.fit(_____, _____).predict(_____)
knn_10_predictions = knn_10.fit(_____, _____).predict(_____)
knn_20_predictions = knn_20.fit(_____, _____).predict(_____)
# Get an accuracy score for each of the models
knn_5_accuracy = accuracy_score(y_test, _____)
knn_10_accuracy = accuracy_score(y_test, _____)
knn_20_accuracy = accuracy_score(y_test, _____)
print("The accuracy of 5, 10, 20 neighbours was {}, {}, {}".format(_____, _____, _____))