Show the understanding of classification using KNN algorithm. The data: Breast cancer data which can be...

Transcribed Image Text:

Show the understanding of classification using KNN algorithm. The data: Breast cancer data which can be loaded using from sklearn.datasets import load_breast cancer cancer = load_breas_cancer() cancer is a class having keys (members), data, target, target_names, feature names, filename cancer.data corresponds to data and cancer.targets corresponds to class labels. You may combine them into a single array using numpy.c_[cancer.data, cancer.target] Separate the file into two data sets half for training (the training set is used to find the class association of test set by using knn algorithm) and remaining half for testing. Implement a knn algorithm and test the class association of the test data and compare with actual labels of the test data and compute the following for k= 6 True positives, False positives, True negatives, and False negatives b. Accuracy, precision, and recall of the classification (Find definitions) c. Confusion matrix (find definition) d. Plot accuracy vs. k plot for the values of k= 4, 5, 6, ... 15 Show the understanding of classification using KNN algorithm. The data: Breast cancer data which can be loaded using from sklearn.datasets import load_breast cancer cancer = load_breas_cancer() cancer is a class having keys (members), data, target, target_names, feature names, filename cancer.data corresponds to data and cancer.targets corresponds to class labels. You may combine them into a single array using numpy.c_[cancer.data, cancer.target] Separate the file into two data sets half for training (the training set is used to find the class association of test set by using knn algorithm) and remaining half for testing. Implement a knn algorithm and test the class association of the test data and compare with actual labels of the test data and compute the following for k= 6 True positives, False positives, True negatives, and False negatives b. Accuracy, precision, and recall of the classification (Find definitions) c. Confusion matrix (find definition) d. Plot accuracy vs. k plot for the values of k= 4, 5, 6, ... 15