使用Sklearn拟合线性回归模型时遇ValueError报错求助

原代码 #

features =["floors", "waterfront", "lat", "bedrooms", "sqft_basement", "view", "bathrooms", "sqft_living15", "sqft_above", "grade", "sqft_living"] 
lm3 = LinearRegression()
lm3.fit(features,["prices"])
lm3.score(features,B)

报错信息 #

ValueError: Expected 2D array, got 1D array instead:
array=['floors' 'waterfront' 'lat' 'bedrooms' 'sqft_basement' 'view' 'bathrooms'
 'sqft_living15' 'sqft_above' 'grade' 'sqft_living'].
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.

完整报错栈：

ValueError                                Traceback (most recent call last)
Cell In[27], line 3
      1 features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"] 
      2 lm3 = LinearRegression()
----> 3 lm3.fit(features,["prices"])
      4 lm3.score(features,B)

File D:\python\Lib\site-packages\sklearn\linear_model\_base.py:648, in LinearRegression.fit(self, X, y, sample_weight)
    644 n_jobs_ = self.n_jobs
    646 accept_sparse = False if self.positive else ["csr", "csc", "coo"]
---> 648 X, y = self._validate_data(
    649     X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True
    650 )
    652 sample_weight = _check_sample_weight(
    653     sample_weight, X, dtype=X.dtype, only_non_negative=True
    654 )
    656 X, y, X_offset, y_offset, X_scale = _preprocess_data(
    657     X,
    658     y,
   (...)
    661     sample_weight=sample_weight,
    662 )

File D:\python\Lib\site-packages\sklearn\base.py:584, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    582         y = check_array(y, input_name="y", **check_y_params)
    583     else:
---> 584         X, y = check_X_y(X, y, **check_params)
    585     out = X, y
    587 if not no_val_X and check_params.get("ensure_2d", True):

File D:\python\Lib\site-packages\sklearn\utils\validation.py:1106, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
   1101         estimator_name = _check_estimator_name(estimator)
   1102     raise ValueError(
   1103         f"{estimator_name} requires y to be passed, but the target y is None"
   1104     )
-> 1106 X = check_array(
   1107     X,
   1108     accept_sparse=accept_sparse,
   1109     accept_large_sparse=accept_large_sparse,
   1110     dtype=dtype,
   1111     order=order,
   1112     copy=copy,
   1113     force_all_finite=force_all_finite,
   1114     ensure_2d=ensure_2d,
   1115     allow_nd=allow_nd,
   1116     ensure_min_samples=ensure_min_samples,
   1117     ensure_min_features=ensure_min_features,
   1118     estimator=estimator,
   1119     input_name="X",
   1120 )
   1122 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
   1124 check_consistent_length(X, y)

File D:\python\Lib\site-packages\sklearn\utils\validation.py:902, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    900     # If input is 1D raise error
    901     if array.ndim == 1:
---> 902         raise ValueError(
    903             "Expected 2D array, got 1D array instead:\narray={}.\n"
    904             "Reshape your data either using array.reshape(-1, 1) if "
    905             "your data has a single feature or array.reshape(1, -1) "
    906             "if it contains a single sample.".format(array)
    907         )
    909 if dtype_numeric and array.dtype.kind in "USV":
    910     raise ValueError(
    911         "dtype='numeric' is not compatible with arrays of bytes/strings."
    912         "Convert your data to numeric values explicitly instead."
    913     )

ValueError: Expected 2D array, got 1D array instead:
array=['floors' 'waterfront' 'lat' 'bedrooms' 'sqft_basement' 'view' 'bathrooms'
 'sqft_living15' 'sqft_above' 'grade' 'sqft_living'].
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.

问题原因 #

直接将特征名称列表传给了fit()和score()方法，而非从Pandas DataFrame中提取对应特征的数值数据。Sklearn的LinearRegression.fit()要求：

• 第一个参数X是二维特征矩阵（每行一个样本，每列一个特征）
• 第二个参数y是一维目标变量数组（每个样本对应的标签）

原代码中，features是字符串列表，属于一维数组，不符合输入维度要求；["prices"]也是列表，不是目标变量的数值序列。

解决方案 #

假设数据存储在名为df的Pandas DataFrame中，需从df提取特征列和目标列后传入模型：

from sklearn.linear_model import LinearRegression
import pandas as pd

# 假设数据存储在df中
features = ["floors", "waterfront", "lat", "bedrooms", "sqft_basement", "view", "bathrooms", "sqft_living15", "sqft_above", "grade", "sqft_living"] 
lm3 = LinearRegression()

# 提取特征矩阵X和目标变量y
X = df[features]
y = df["prices"]

# 拟合模型
lm3.fit(X, y)

# 计算评分（替换原代码中未定义的B为真实目标变量y）
model_score = lm3.score(X, y)
print(model_score)

关键说明 #

1. df[features]返回DataFrame，是符合要求的二维特征矩阵
2. df["prices"]返回Series，是一维目标变量数组，满足Sklearn输入要求
3. 原代码中B未定义，需替换为真实目标变量数据

内容的提问来源于stack exchange，提问作者Legofan35664