我正在尝试使用线性回归模型预测值。但是,当我使用sklearn中的.predict时,我找不到在没有出现数据类型错误的情况下为X插入数据的方法。
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kaggle笔记本中运行代码时,出现以下错误:
---------------------------------------------------------------------------
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')
我将不胜感激:)
答案 0 :(得分:1)
假设您的因变量是连续的,请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,您不能仅使用+
连接一个字符串和一个数组,下面的这些否定示例在问题中给您相同的错误:
"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')
您可以这样做:
print("Predicted missing KitchenQual value: " + str(pred[0]))
Predicted missing KitchenQual value: -0.11176904834490986