Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- import codecademylib3_seaborn
- from sklearn.datasets import load_breast_cancer
- from sklearn.model_selection import train_test_split
- from sklearn.neighbors import KNeighborsClassifier
- import matplotlib.pyplot as plt
- breast_cancer_data = load_breast_cancer()
- training_data, validation_data, training_labels, validation_labels = train_test_split(breast_cancer_data.data,breast_cancer_data.target,train_size=0.8,random_state = 19)
- accuracies = []
- for k in range(1,101):
- classifier = KNeighborsClassifier(n_neighbors = k)
- classifier.fit(training_data,training_labels)
- accuracies.append(classifier.score(validation_data,validation_labels))
- k_list = range(1,101)
- plt.plot(k_list,accuracies)
- plt.xlabel('k')
- plt.ylabel('Validation Accuracy')
- plt.title('Breast Cancer Classifier Accuracy')
- plt.show()
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement