PYTHON

searching for best k values in knn

# Create neighbors
neighbors = np.arange(1, 13)
train_accuracies = {}
test_accuracies = {}

for neighbor in neighbors:
  
  	# Set up a KNN Classifier
  	knn = KNeighborsClassifier(n_neighbors=neighbor)
  
  	# Fit the model
  	knn.fit(X_train, y_train)
  
  	# Compute accuracy
  	train_accuracies[neighbor] = knn.score(X_train, y_train)
  	test_accuracies[neighbor] = knn.score(X_test, y_test)
print(neighbors, '
', train_accuracies, '
', test_accuracies)
# Add a title
plt.title("KNN: Varying Number of Neighbors")

# Plot training accuracies
plt.plot(neighbors, train_accuracies.values(), label="Training Accuracy")

# Plot test accuracies
plt.plot(neighbors, test_accuracies.values(), label="Testing Accuracy")

plt.legend()
plt.xlabel("Number of Neighbors")
plt.ylabel("Accuracy")

# Display the plot
plt.show()