我收到一個 ValueError：“ValueError：標簽數=16512 與樣本數=16339 不匹配”

Question

我正在嘗試機器學習並且我是新手，所以我不知道為什么會收到此錯誤： ValueError: Number of labels=16512 does not match number of samples=16339我搜索了它，但沒有任何幫助。 有人可以幫我解決這個問題嗎？ 我不知道為什么它會這樣做，我認為我做的一切都是正確的。 我試圖用這個來預測房價。

from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.model_selection import train_test_split

train = pd.read_csv('housing.csv')
X = train.drop(columns=["median_house_value", "ocean_proximity"])
y = train["median_house_value"]

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)

model = DecisionTreeClassifier()
X_train = X_train.dropna()
y_train = y_train.dropna()

model.fit(X_train, y_train)

這是我的錯誤信息：

ValueError                                Traceback (most recent call last)
<ipython-input-43-4691a6b66d80> in <module>
     17 y_train = y_train.dropna()
     18 
---> 19 model.fit(X_train, y_train)

c:\users\zhang\appdata\local\programs\python\python38\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    888         """
    889 
--> 890         super().fit(
    891             X, y,
    892             sample_weight=sample_weight,

c:\users\zhang\appdata\local\programs\python\python38\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    270 
    271         if len(y) != n_samples:
--> 272             raise ValueError("Number of labels=%d does not match "
    273                              "number of samples=%d" % (len(y), n_samples))
    274         if not 0 <= self.min_weight_fraction_leaf <= 0.5:

ValueError: Number of labels=16512 does not match number of samples=16339```

Answer 1

您可以嘗試以下方法嗎？ 我對這種方法沒有問題：

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.model_selection import train_test_split

data = pd.read_csv('housing.csv')

prices = data['median_house_value']
features = data.drop(['median_house_value', 'ocean_proximity'], axis = 1)

---

prices.shape
(20640,)

features.shape
(20640, 8)

---

X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.2, random_state=42)

X_train = X_train.dropna()
y_train = y_train.dropna()

---

y_train.shape
(16512,)

X_train.shape
(16512, 8)

---

model.fit(X_train, y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
                       max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort=False,
                       random_state=None, splitter='best')