# kNN Analysis in ScikitLearn k-Nearest Neighbors analysis is a useful supervised learning method for classification. A number of data points with any number of features (above zero) are passed into the analysis, each classified to a specific type. Then, test data is passed into the algorithm, and each data point is classified based on it's nearest neighbors. The "k" in kNN is the number of neighbors each test datum is compared to so a determination can be reached. ```python # import desired packages... from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from matplotlib import pyplot as plt breast_cancer_data = load_breast_cancer() print(breast_cancer_data.target) print(breast_cancer_data.target_names) ``` ``` [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1] ['malignant' 'benign'] ``` Lots of data! Remember, the reason why a computer is doing this is because of the huge number of features within the dataset. The next part shows off an awesome part of Skikit: separating a large data set into training and testing sets for use later. ```python training_data, validation_data, training_labels, validation_labels = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state = 100) # test to ensure data loaded correctly; if so, amounts are equal print(len(training_data), len(training_labels)) ``` ``` 455 455 ``` An important part of using a kNN analysis is choosing a good *k*. Too small of a *k* can skew the outcome with outliers, while too large of a *k* will lose the interesting parts of the dataset. The following loops through and shows the relative accuracy of different k-values. This can be helpful if you have the spare computing power. To just make one model, substitute *i* in the following `for` loop with your k-value, and run the first two lines of the loop. The third line will provide an estimation of the trained algorithm's accuracy. ```python k_list = list(range(1, 100)) accuracies = [] for i in range(1, 100): classifier = KNeighborsClassifier(n_neighbors = i) classifier.fit(training_data, training_labels) accuracies.append(classifier.score(validation_data, validation_labels)) plt.plot(k_list, accuracies) plt.xlabel("k") plt.ylabel("Validation Accuracy") plt.title("Breast Cancer Classifier Accuracy") plt.show() ``` ![png](https://github.com/octokatt/wiki/tree/dafb14549b591f1b0049d5daaf79d06e56aefada/python/output_6_0.png) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://katt.gitbook.io/wiki/python/machine_learning/knn-notes.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.