简单线性回归评分中的 FitFailedWarning cross_val_score

Question

我使用的是从 Internet 下载的非常简单的 csv 文件，只有两列。第一列是“MonthsExperience”，类似于“3、3、4、4、5、6...”，第二列类似于“424、387、555、59、533...”。

为了训练，我正在尝试获取 RandomForestRegressor 模型的 cross_val_score 简单线性回归。

代码如下：

import numpy as np
import pandas as pd

data = pd.read_csv("Blogging_Income.csv")

X = data["MonthsExperience"]
y = data["Income"]

from sklearn.ensemble import RandomForestRegressor

rfr = RandomForestRegressor()

from sklearn.model_selection import cross_val_score

cv_r2 = cross_val_score(rfr, X, y, cv = 5, scoring = None)
print(cv_r2)

我从 sklearn 收到一条长长的白色警告，指出所有结果都变成了 NaN，因为模型无法拟合。我得到的warning/error的上半部分是这样的：

[nan nan nan nan nan]
C:\Users\----\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 304, in fit
    X, y = self._validate_data(X, y, multi_output=True,
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\base.py", line 433, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 871, in check_X_y
    X = check_array(X, accept_sparse=accept_sparse,
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\Users\----\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 694, in check_array
    raise ValueError(
ValueError: Expected 2D array, got 1D array instead:
array=[ 6.  6.  7.  8.  8.  9.  9. 10. 11. 11. 12. 12. 12. 13. 13. 14. 14. 15.
 15. 16. 16. 17. 18. 18.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

数组的形状似乎有误，但我不明白为什么。我也不明白如何使用 array.reshape 来完成这项工作。

Answer 1

与任何其他机器学习模型类似，RandomForest 要求您的数据是二维的。即使你只有一个特征，你的 X 也必须是 N x 1，而不是长度为 N 的向量。

您可以使用 numpy 重塑数据

X = np.array(X).reshape(-1, 1)

简单线性回归评分中的 FitFailedWarning cross_val_score

FitFailedWarning in Simple Linear Regression scoring with cross_val_score

python

machine-learning

scikit-learn

regression

cross-validation