使用pywebio时LinearRegression predict()特征数不匹配报错的解决

我用pywebio给机器学习程序做了个小界面，不用界面的时候跑线性回归的predict()没问题。界面从用户那获取age（年龄）和salary（薪资）两个数值，存成numpy数组后转成二维数组（之前踩过数组形状的坑），但现在调用predict()时报错：ValueError: X has 1 features, but LinearRegression is expecting 2 features as input。求修复方法。

UI代码 #

age = int(input("Enter your age:", type=NUMBER))
salary = int(input("Enter your salary:", type=NUMBER))

entry = np.array([age, salary])
reshaped_entry = entry.reshape(-1, 1)

estimate = regr.predict(reshaped_entry)    

错误信息 #

ValueError                                Traceback (most recent call last)
Input In [21], in <cell line: 22>()

Input In [21], in retirement_ui()

File ~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py:362, in LinearModel.predict(self, X)
    348 def predict(self, X):
    349     """
    350     Predict using the linear model.
    351 
   (...)
    360         Returns predicted values.
    361     """
--> 362     return self._decision_function(X)

File ~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py:345, in LinearModel._decision_function(self, X)
    342 def _decision_function(self, X):
    343     check_is_fitted(self)
--> 345     X = self._validate_data(X, accept_sparse=["csr", "csc", "coo"], reset=False)
    346     return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_

File ~\anaconda3\lib\site-packages\sklearn\base.py:585, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    582     out = X, y
    584 if not no_val_X and check_params.get("ensure_2d", True):
--> 585     self._check_n_features(X, reset=reset)
    587 return out

File ~\anaconda3\lib\site-packages\sklearn\base.py:400, in BaseEstimator._check_n_features(self, X, reset)
    397     return
    399 if n_features != self.n_features_in_:
--> 400     raise ValueError(
    401         f"X has {n_features} features, but {self.__class__.__name__} "
    402         f"is expecting {self.n_features_in_} features as input."
    403     )

ValueError: X has 1 features, but LinearRegression is expecting 2 features as input.

修复方法 #

问题出在数组重塑的方向上：

• 当前entry.reshape(-1, 1)会把[age, salary]这个长度为2的一维数组变成2行1列的二维数组，模型会判定这是2个样本、每个样本1个特征，和训练时要求的2个特征不匹配。
• 正确做法是把数组塑造成1行2列的二维数组，对应1个样本、2个特征，和训练数据的特征数量一致。

修改后的代码：

age = int(input("Enter your age:", type=NUMBER))
salary = int(input("Enter your salary:", type=NUMBER))

entry = np.array([age, salary])
# 改成reshape(1, -1)，让numpy自动计算列数
reshaped_entry = entry.reshape(1, -1)
# 或者一步到位直接创建二维数组：entry = np.array([[age, salary]])

estimate = regr.predict(reshaped_entry)    

解释：

• reshape(1, -1)会生成形状为(1,2)的数组，正好符合模型需要的「1个样本、2个特征」输入格式。
• 直接用np.array([[age, salary]])创建二维数组，省去重塑步骤，逻辑更直观。

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