metrics import cohen_kappa_score from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from keras.models import Sequential from keras.layers import Dense # generate and prepare
Error metrics enable us to evaluate the performance of a machine learning model on a particular dataset. There are various error metric models depending upon the class of algorithm. We have the Confusion Matrix to deal with and evaluate Classification algorithms. While R square is an important err...
from sklearn.metrics import accuracy_score from matplotlib import pyplot # load dataset data = read_csv('pima-indians-diabetes.data.csv', header=None) values = data.values # configure bootstrap n_iterations = 1000 n_size = int(len(data) * 0.50) # run bootstrap stats = list() for ...
This is different from hypothesis tests that make use of resampling methods as more, if not all, of the dataset is made available as a test set during evaluation (which introduces its own problems from a statistical perspective). This provides less of an opportunity to compare the performance ...