如何解决引发ValueError(“错误的输入形状{0}".format(shape))； ValueError:输入形状错误(977，57) [英] How to resolve raise ValueError("bad input shape {0}".format(shape)); ValueError: bad input shape (977, 57)

问题描述

一个数据集具有多个2500 rows和22 columns(包括年龄列).我已经完成了SVR的所有过程.它继续.但是我仍然要面对一个错误.那是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?

A dataset has more than 2500 rows and 22 columns including the age column. I have completed all of the processes for SVR. It going on. But I am still having to face an error. That is raise ValueError("bad input shape {0}".format(shape)), ValueError: bad input shape (977, 57). My input is SupportVectorRefModel.fit(X_train, y_train). How can I resolve this problem?

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)

食物:

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

推荐答案

您需要将train_X, train_y传递给您的.fit函数.您当前正在传入X_train，它是删除age列之前 的数据集.

You need to pass in train_X, train_y to your .fit function. You're currently passing in X_train which is the dataset before you remove the age column.

这应该是

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

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