我正在尝试在python中的SVM,Logistic回归,MLP和随机森林回归之间调整参数,但是它显示了SVM和Logistic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\0.5-1\0.5-1_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'
如何解决此错误?
答案 0 :(得分:0)
由于目标变量本质上是连续的,因此不能使用logisticRegression
,而不能使用linearRegression
或SVR
或SVC
。
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
希望这会有所帮助!