Matplotlib.pyplot報錯“ValueError:xandymustbethesamesize”回歸分析

我是資料分析的新手，我一直在使用 matplotlib、numpy 和 pandas 進行回歸分析編碼。但是，我遇到了一些麻煩，并盡我所能通過 Stackoverflow 和其他網站找到解決問題的方法，但我還是做不到。

這是代碼

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Training_Dataset = pd.read_csv("https://raw.githubusercontent.com/kuroisep/Problem-1-Data-Analytic/main/A-train.csv?token=GHSAT0AAAAAABZ7O6G34ZTK4PMQGGZLRVS4Y2LAJSQ")
Training_Dataset = Training_Dataset.dropna()
X_train = np.array(Training_Dataset.iloc[:, :-1].values) # Independent Variable
y_train = np.array(Training_Dataset.iloc[:, 1].values) # Dependent Variable

Testing_Datatset = pd.read_csv("https://raw.githubusercontent.com/kuroisep/Problem-1-Data-Analytic/main/A-test.csv?token=GHSAT0AAAAAABZ7O6G2PRKTCT6YBKLZZSRWY2LAK2Q")
Testing_Dataset = Testing_Dataset.dropna()
X_test = np.array(Testing_Dataset.iloc[:, :-1].values) # Independent Variable
y_test = np.array(Testing_Dataset.iloc[:, 1].values) # Dependent Variable

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

accuracy = regressor.score(X_test, y_test)
print('Accuracy = '  str(accuracy))

plt.style.use('seaborn')
plt.scatter(X_test, y_test, color = 'red', marker = 'o', s = 35, alpha = 0.5,
          label = 'Test data')
plt.plot(X_train, regressor.predict(X_train), color = 'blue', label='Model Plot')
plt.title('Predicted Values vs Inputs')
plt.xlabel('Inputs')
plt.ylabel('Predicted Values')
plt.legend(loc = 'upper left')
plt.show()

這是語法錯誤詳細資訊

/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
   4389         y = np.ma.ravel(y)
   4390         if x.size != y.size:
-> 4391             raise ValueError("x and y must be the same size")
   4392 
   4393         if s is None:

ValueError: x and y must be the same size

我的 X_test 值為 (shape = (6,8))

array([[-2.474000e 01, -1.550000e 00,  9.105000e 01,  2.401980e 03,
        -1.520000e 00,  1.360000e 01,  5.660000e 00,  1.059000e 01],
       [ 1.075400e 02, -2.869000e 01, -8.259000e 01,  2.578915e 04,
         5.290000e 00, -8.560000e 00,  1.490000e 00, -4.730000e 00],
       [ 4.508000e 01,  9.662000e 01,  5.185000e 01,  1.280000e 00,
         3.580000e 00,  5.200000e-01, -6.860000e 00, -7.800000e-01],
       [-1.228100e 02,  1.779000e 01, -1.828500e 02,  2.928970e 03,
        -1.210000e 00, -2.060000e 00,  9.680000e 00, -8.590000e 00],
       [ 7.761000e 01, -7.230000e 01,  9.728000e 01,  1.917394e 04,
        -9.290000e 00,  8.600000e-01,  7.060000e 00, -8.060000e 00],
       [-4.401000e 01,  1.316500e 02,  6.988000e 01,  1.778310e 03,
        -1.375000e 01, -1.475000e 01, -1.227000e 01, -8.300000e-01]])

我的 y_test 值為 (shape=(6,))

array([ -1.55, -28.69,  96.62,  17.79, -72.3 , 131.65])

如果我的問題看起來很老，我很抱歉。謝謝你的好意

uj5u.com熱心網友回復：

對于第二個圖，regressor.predict(X_train)將給出預測y_train值，因此再次將預測值與實際值進行比較：

plt.plot(y_train, regressor.predict(X_train), color = 'blue', label='Model Plot')

輸出：

順便說一句，您的訓練測驗拆分不正確。您設定x2為目標變數，但x2也包含在training集合中，這就是模型完美擬合并給出100%準確性的原因，但這超出了這個問題的范圍。

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