首页X = df.drop('Outcome', axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=35 / 769,random_state=0) lr = LogisticRegression(random_state=0, max_iter=1000) lr.fit(X_train, y_train) y_pred = lr.predict(X_test) print('Logistic 回归模型') print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) y_ = np.array(y_test) print('Logistic 回归预测结果:', lr.predict(X_test)) print('原始数据集的真实结果: ', y_) print('模型得分:{:.2f}'.format(lr.score(X_test, y_test))) modelscore = format(lr.score(X_test, y_test)) if float(modelscore) >= 0.88: print("模型预测准确率较高，适合用来预测糖尿病") else: print("模型预测准确率较低，不宜用来预测糖尿病")画逻辑回归图

X = df.drop('Outcome', axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=35 / 769,random_state=0) lr = LogisticRegression(random_state=0, max_iter=1000) lr.fit(X_train, y_train) y_pred = lr.predict(X_test) print('Logistic 回归模型') print(confusion_matrix(y_test, y_pred)) print(classification_report(y_test, y_pred)) y_ = np.array(y_test) print('Logistic 回归预测结果:', lr.predict(X_test)) print('原始数据集的真实结果: ', y_) print('模型得分:{:.2f}'.format(lr.score(X_test, y_test))) modelscore = format(lr.score(X_test, y_test)) if float(modelscore) >= 0.88: print("模型预测准确率较高，适合用来预测糖尿病") else: print("模型预测准确率较低，不宜用来预测糖尿病")画逻辑回归图

时间: 2023-12-24 07:22:11 浏览: 74

2-Control.rar_control valve _decide_heater_site:www.pudn.com_val

For example, heating up the temperature in a room is a process that has the specific, desired outcome to reach and maintain a defined temperature (e.g. 20°C), kept constant over time. Here, the temperature is the controlled variable. At the same time, it is the input variable since it is measured by a thermometer and used to decide whether to heat or not to heat. The desired temperature (20°C) is the setpoint. The state of the heater (e.g. the setting of the valve allowing hot water to flow through it) is called the manipulated variable since it is subject to control action

抱歉，我是一名语言模型，无法画图。但是，你可以使用Python中的Matplotlib库来画出逻辑回归图。你可以使用以下代码： ``` import matplotlib.pyplot as plt X = df.drop('Outcome', axis=1) y = df['Outcome'] lr = LogisticRegression(random_state=0, max_iter=1000) lr.fit(X, y) plt.figure(figsize=(10, 6)) plt.scatter(X[y==0]['BMI'], X[y==0]['Glucose'], label='Negative') plt.scatter(X[y==1]['BMI'], X[y==1]['Glucose'], label='Positive') x = np.linspace(0, 60, 1000) y = -(lr.intercept_[0] + lr.coef_[0][0]*x) / lr.coef_[0][1] plt.plot(x, y, color='black', linestyle='--', label='Decision Boundary') plt.xlabel('BMI') plt.ylabel('Glucose') plt.legend() plt.show() ``` 这段代码可以画出以BMI和Glucose为横纵坐标的散点图，其中糖尿病患者和非患者用不同颜色表示，同时还可以画出逻辑回归的决策边界。

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