How to resolve raise ValueError("bad input shape {0}".format(shape)); ValueError: bad input shape (977, 57)

Question

一个数据集有超过 2500 rows 和 22 columns 包括年龄列。我已经完成了 SVR 的所有流程。它在继续。但我仍然不得不面对一个错误。即raise ValueError("bad input shape {0}".format(shape)), ValueError: bad input shape (977, 57)。我的输入是 SupportVectorRefModel.fit(X_train, y_train)。我该如何解决这个问题？

from sklearn.model_selection 
import train_test_split 
from sklearn.svm import SVR 

X_train, y_train = dataset.loc[:1000], dataset.loc[:1000] 
X_test, y_test = dataset.loc[1001], dataset.loc[1001] 
train_X, train_y = X_train.drop(columns=['age']), y_train.pop('age')
test_X, test_y = X_test.drop(columns=['age']), y_test.pop('age')

SupportVectorRefModel = SVR()
SupportVectorRefModel.fit(X_train, y_train)

输出：

raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (977, 57)

Answer 1

您需要将 train_X, train_y 传递给您的 .fit 函数。您当前传递的是 X_train 数据集 在您删除 age 列之前 。

这是应该的

SupportVectorRefModel = SVR()
SupportVectorRefModel.fit(train_x, train_y)

How to resolve raise ValueError("bad input shape {0}".format(shape)); ValueError: bad input shape (977, 57)

How to resolve raise ValueError("bad input shape {0}".format(shape)); ValueError: bad input shape (977, 57)

python

pandas

machine-learning

scikit-learn

regsvr32