Procurando os melhores valores de K em 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, '\n', train_accuracies, '\n', 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()
josh.ipynb