Python 为什么我的逻辑回归模型的准确率超过100%？

我正在研究一个数据集，该数据集由几个医学预测变量和一个目标变量组成，用于分类患者是否患有糖尿病。我在构建模型时没有使用scikit学习/sklearn库。我已将链接附加到下面的数据集

我已经训练和测试了模式，但我一直获得100%以上的准确性。 我是这个领域的新手，所以如果我犯了愚蠢的错误，我向你道歉。下面是我的代码（我只使用Glucose和DiabetesPedigreeFunction）来分类

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    %matplotlib inline
    df = pd.read_csv('diabetes.csv')
    df.head()

    df.drop(['BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 
    'Pregnancies', 'Age'], axis = 1, inplace=True)
    df

    positive = df[df['Outcome'].isin([1])]
    negative = df[df['Outcome'].isin([0])]

    fig, ax = plt.subplots(figsize=(12,8))
    ax.scatter(positive['DiabetesPedigreeFunction'],positive['Glucose'], 
    s=50, c='b', marker='o', label='Diabetes')
    ax.scatter(negative['DiabetesPedigreeFunction'],negative['Glucose'], 
    s=50, c='r', marker='x', label='Not Diabetes')
    ax.legend()

    def sigmoid(x):
      return 1/(1 + np.exp(-x))

    
    nums = np.arange(-10, 10, step=1)
    fig, ax = plt.subplots(figsize=(12,8))
    ax.plot(nums, sigmoid(nums), 'r')

    def cost(theta, X, y):
        theta = np.matrix(theta)
        X = np.matrix(X)
        y = np.matrix(y)
        first = np.multiply(-y, np.log(sigmoid(X * theta.T)))
        second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))
        return np.sum(first - second) / (len(X))

        X.shape, theta.shape, y.shape
        
        cost(theta, X, y)

        def gradient(theta, X, y):
            theta = np.matrix(theta)
            X = np.matrix(X)
            y = np.matrix(y)

            parameters = int(theta.ravel().shape[1])
            grad = np.zeros(parameters)

            error = sigmoid(X * theta.T) - y

            for i in range(parameters):
                term = np.multiply(error, X[:,i])
                grad[i] = np.sum(term) / len(X)

            return grad

    gradient(theta, X, y)
    import scipy.optimize as opt
    result = opt.fmin_tnc(func=cost, x0=theta, fprime=gradient, args=(X, 
    y))

    cost(result[0], X, y)

    def predict(theta, X):
        probability = sigmoid(X * theta.T)
        return [1 if x >= 0.5 else 0 for x in probability]

    theta_min = np.matrix(result[0])
    predictions = predict(theta_min, X)
    correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 
    for (a, b) in zip(predictions, y)]
    accuracy = (sum(map(int, correct)) % len(correct))
    print ('accuracy = {}%'.format(accuracy))

---

我的准确率是574%。我需要一些反馈。提前感谢。

您使用的是mod而不是division

准确度的计算应如下所示：

精度=总和（正确）/len（正确）

谢谢！我错过了。