运行中的 SVM 错误和 python 中的逻辑回归

Question

我正在尝试在 python 中调整 SVM、逻辑回归、MLP 和随机森林回归之间的参数，但它显示 SVM 和逻辑回归的值错误。我的样本数据是这样的：

Wavelength    Phase_velocity     Shear_wave_velocity
1.50              202.69          240.73
1.68              192.72          240.73
1.79              205.54          240.73
17.08             218               229
16.73             243               269
17.72             245               269
16.72             212               253
17.26             214               253
........

示例代码为：

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split


df = pd.read_csv("0.5-1.csv")
df.head()

X = df[['wavelength', 'phase velocity']]
y = df['shear wave velocity']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

print (len(X_train),len(X_test),len(y_train),len(y_test))

lr = LogisticRegression(solver='liblinear',multi_class='ovr')
lr.fit(X_train, y_train)
print (lr.score(X_test, y_test))

svm = SVC(gamma='auto')
svm.fit(X_train, y_train)
print (svm.score(X_test, y_test))

mlp = MLPRegressor(hidden_layer_sizes=(50,50,50), max_iter=2000, activation='relu')
mlp.fit(X_train,y_train)
print (mlp.score(X_test, y_test))

rf = RandomForestRegressor(n_estimators=40)
rf.fit(X_train, y_train)
print (rf.score(X_test, y_test))

错误是这样的：

Traceback (most recent call last):
  File "G:\My Drive\ANN\test[=14=].5-1[=14=].5-1_tunecode.py", line 23, in <module>
    lr.fit(X_train, y_train)
  File "C:\Users\sadia\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\linear_model\logistic.py", line 1533, in fit
    check_classification_targets(y)
  File "C:\Users\sadia\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils\multiclass.py", line 169, in check_classification_targets
    raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous'

如何解决这个错误？

Answer 1

由于您的目标变量本质上是连续的，因此您不能使用 logisticRegression，请使用 linearRegression 或 SVR 而不是 SVC。

from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR

希望对您有所帮助！

运行中的 SVM 错误和 python 中的逻辑回归

Error in running SVM and Logistic Regression in python

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运行 中的 SVM 错误和 python 中的逻辑回归

Error in running SVM and Logistic Regression in python

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运行中的 SVM 错误和 python 中的逻辑回归