错误:发现数组与dim 3。估计预期

I have a 14x5 data matrix titled data. The first column (Y) is the dependent variable followed by 4 independent variables (X,S1,S2,S3). When trying to fit a regression model to a subset of the independent variables ['S2'][:T] I get the following error:

我有一个14x5的数据矩阵，名为data。第一列(Y)是因变量，其次是4个自变量(X,S1,S2,S3)。当试图将一个回归模型拟合到一个独立变量子集['S2'][:T]时，我得到以下错误:

ValueError: Found array with dim 3. Estimator expected <= 2.

I'd appreciate any insight on a fix. Code below.

我很欣赏你对这个问题的见解。下面的代码。

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression


data = pd.read_csv('C:/path/Macro.csv')
T=len(data['X'])-1

#Fit variables
X = data['X'][:T]
S1 = data['S1'][:T]
S2 = data['S2'][:T]
S3 = data['S3'][:T]
Y = data['Y'][:T]

regressor = LinearRegression()
regressor.fit([[X,S1,S2,S3]], Y)

1 个解决方案

#1

You are passing a 3-dimensional array as the first argument to fit(). X, S1, S2, S3 are all Series objects (1-dimensional), so the following

您正在传递一个三维数组作为第一个参数来匹配()。X, S1, S2, S3都是序列对象(1维)，所以如下。

[[X, S1, S2, S3]]

is 3-dimensional. sklearn estimators expect an array of feature vectors (2-dimensional).

是三维的。sklearn估计器期望一个特征向量数组(二维)。

Try something like this:

试试这样:

# pandas indexing syntax
# data.ix[ row index/slice, column index/slice ]

X = data.ix[:T, 'X':]  # rows up to T, columns from X onward
y = data.ix[:T, 'Y']   # rows up to T, Y column
regressor = LinearRegression()
regressor.fit(X, y)

#1