我在尝试使用线性回归模型预测一个值。然而，当我使用 sklearn 的 .predict 函数时，我无法找到一种方法来输入 X 的数据而不导致数据类型错误。

from sklearn import linear_modelKitchenQual_X = KitchenQual_df[["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea"]]KitchenQual_Y = KitchenQual_df["dummy_KitchenQual"]regr_KitchenQual = linear_model.LinearRegression()regr_KitchenQual.fit(KitchenQual_X, KitchenQual_Y)print("Predicted missing KitchenQual value: " + regr_KitchenQual.predict(df_both[["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea"]].loc[[1555]]))

在我的 Kaggle 笔记本中运行代码时，我收到了以下错误：

---------------------------------------------------------------------------UFuncTypeError                            Traceback (most recent call last)<ipython-input-206-1f022a48e21c> in <module>----> 1 print("Predicted missing KitchenQual value: " + regr_KitchenQual.predict(df_both[["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea"]].loc[[1555]]))UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U37'), dtype('<U37')) -> dtype('<U37')

任何帮助我都会非常感激 🙂

回答：

假设你的因变量是连续的，使用示例数据并重复你的步骤：

from sklearn import linear_modelimport numpy as npimport pandas as pdKitchenQual_df = pd.DataFrame(np.random.normal(0,1,(2000,6)))KitchenQual_df.columns = ["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea","dummy_KitchenQual"]KitchenQual_X = KitchenQual_df[["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea"]]KitchenQual_Y = KitchenQual_df["dummy_KitchenQual"]regr_KitchenQual = linear_model.LinearRegression()regr_KitchenQual.fit(KitchenQual_X, KitchenQual_Y)pred = regr_KitchenQual.predict(KitchenQual_df[["OverallQual", "YearBuilt", "YearRemodAdd", "GarageCars", "GarageArea"]].loc[[1555]])

预测结果是一个数组，你不能直接使用 + 将字符串和数组连接起来，以下负面示例会给你相同的错误：

"a" + np.array(['b','c'])"a" + np.array([1,2])UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U1'), dtype('<U1')) -> dtype('<U1')

你可以这样做：

print("Predicted missing KitchenQual value: " + str(pred[0]))Predicted missing KitchenQual value: -0.11176904834490986