defmain():diabetes = datasets.load_diabetes()# Use only one featurediabetes_X = diabetes.data[:, np.newaxis,2] diabetes_X = scale(diabetes_X) diabetes_y = scale(diabetes.target) diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:]# diabetes_y_train = diabetes.t...
# 需要导入模块: from sklearn import datasets [as 别名]# 或者: from sklearn.datasets importload_diabetes[as 别名]defget_sample_dataset(dataset_properties):"""Returns sample dataset Args: dataset_properties (dict): Dictionary corresponding to the properties of the dataset used to verify the estima...
This recipe show you how to load a CSV file from a URL, in this case thePima Indians diabetesclassification dataset from theUCI Machine Learning Repository. From the prepared X and y variables, you can train a machine learning model. # Load the Pima Indians diabetes dataset from CSV URL im...
Additionally, whereas cardiovascular disease is the second leading cause of all death in Japan (15), the number of Japanese people with type 2 diabetes is estimated to be no fewer than 6.8 million (16); thus, as is the case in Western people, these are serious health problems in Japan. ...
split(X) elif data_type == 'diabetes': X, y = datasets.load_diabetes(return_X_y=True) splits = model_selection.KFold(n_splits=2, random_state=8).split(X) else: raise exceptions.UserError('Unknown dataset type {}'.format(dataset_properties['type'])) return X, y, splits ...
The placental DNA samples represented all three trimesters of normal pregnancies (n 5 9, 8 and 8, respectively), and term cases of late gestational complications (each, n 5 8) of the mother (severe preeclampsia, PE; gestational diabetes, GD) or the newborn (small-for-gestational age, ...
However the c-statistics did not change significantly (螖AUC 0.0094, p=0.099).PGS is a better predictor of post-MI prognosis than FG or 2hG in patients without known diabetes.doi:10.1093/eurjpc/zwac056.058S. ChattopadhyayB. SomanA. George...
Tests should be changed to another available regression dataset likeload_diabetesor use proposedsynthetic regression dataset. ContributorAuthor StrikerRUSclosed this ascompletedDec 26, 2021 StrikerRUSadded theduplicatelabelDec 26, 2021 microsoftlocked asresolvedand limited conversation to collaboratorsDec 23,...
filename ="datasets/pima-indians-diabetes.data"targets = [0,1]# Create the networknn = NeuralNetwork() nn.loadDataset(filename) nn.normalize() nn.createNetwork([2,3], targets) numCorrect =0# Make the predictionsforiinrange(1):
Shows how to use the CostSensitiveClassifier. """# load a datasetdata_file = helper.get_data_dir() + os.sep +"diabetes.arff"helper.print_info("Loading dataset: "+ data_file) loader = Loader("weka.core.converters.ArffLoader")