我们在第10行train, test = train_test_split(data, test_size=0.2)做了什么？

import pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.naive_bayes import GaussianNB as gnbfrom sklearn.metrics import accuracy_scoredata = pd.read_csv(r'C:\Users\avina\OneDrive\Desktop\New folder\program2_diabetes-20191105T144141Z-001\program2_diabetes\die.csv')print (data.describe())features = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'BMI', 'Age', 'Insulin', 'DiabetesPedigreeFunction']target = 'Class'train, test = train_test_split(data, test_size=0.2)clf = gnb().fit(train[features], train[target]) y_predicted = clf.predict(test[features])print ("Accuracy ",round(accuracy_score(test[target], y_predicted)*100,2)," %")

回答：

train, test = train_test_split(data, test_size=0.2)

它将你的数据框分成了两个较小的数据框，一个包含原始数据的80%（训练集），另一个包含20%（测试集）。

你可以通过检查形状并使用以下代码确认所有数据都在：

train.shape[0] + test.shape[0] == data.shape[0]