SKlearn多线性回归-->dtype误差

Question

我正试图使用线性回归模型来预测一个值。然而，当我使用sklearn中的.prdict时，我找不到一种方法来插入X的数据而不出现数据类型错误。

from sklearn import linear_model
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)
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')

如果有任何帮助，我将不胜感激：(

Answer 1

假设因变量是连续的，使用示例数据并重复您的步骤：

from sklearn import linear_model
import numpy as np
import pandas as pd
KitchenQual_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]])

预测是一个数组，你不能只使用+连接字符串和数组，下面的反例会给你同样的错误：

"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')

你可以做：

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