sklearn 多重線性回歸 --> dtype 錯誤

Question

我正在嘗試使用線性回歸模型預測一個值。 但是，當我使用 sklearn 中的 .predict 時，我找不到一種方法來插入 X 的數據而不會出現數據類型錯誤。

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 notebook 中運行代碼時，我收到以下錯誤：

---------------------------------------------------------------------------
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')

我將不勝感激任何幫助 ：）

Answer 1

假設您的因變量是連續的，使用示例數據並重復您的步驟：

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]])

預測是一個數組，您不能只使用+連接字符串和數組，下面的這些負面示例在問題中給出了相同的錯誤：

"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