如何获得预测值以及测试数据，并可视化实际值与预测值？

Question

from sklearn import datasets
import numpy as np
import pandas as pd from sklearn.model_selection
import train_test_split
from sklearn.linear_model import Perceptron

data = pd.read_csv('student_selection.csv')

x = data[['Average','Pass','Division','Domicile']]
y = data[['Selected']]

x_train,x_test,y_train,y_test train_test_split(x,y,test_size=1,random_state=0)

ppn = Perceptron(eta0=1.0, fit_intercept=True, max_iter=1000, n_iter_no_change=5, random_state=0)

ppn.fit(x_train, y_train)

y_pred = ppn.predict(x_train)

x_train['Predicted'] = pd.Series(y_pred)

如何将预测的实际vs视为一个表格和一个绘图x_train是我得到的预测值，但我无法将其与实际数据合并以查看偏差

Answer 1

How to see the actual vs predicted as a table and along with a plot?

只需运行：

y_predict= pnn.predict(x)

data['y_predict'] = y_predict

并在数据框中显示该列，如果要绘制该列，可以使用：

import matplotlib.pyplot as plt
plt.scatter(data['Selected'], data['y_predict'])
plt.show()