在我的决策树模型上获得 100% 的准确度

Question

这是我的代码，无论测试规模有多大，它总是返回 100% 的准确率。我使用了 train_test_split 方法，所以我认为不应该有任何重复的数据。有人可以检查我的代码吗？

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


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()
X_test = X_test.dropna()
y_test = X_test.dropna()

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

y_train.shape
(16512,)

X_train.shape
(16512, 8)


predictions = model.predict(X_test)
score = model.score(y_test, predictions)
score 

Answer 1

编辑：我已经修改了我的答案，因为我发现了多个问题。请复制粘贴以下代码以确保不留下任何错误。

问题 -

1. 您正在使用 DecisionTreeClassifier 而不是 DecisionTreeRegressor 来解决回归问题。
2. 在进行测试列车拆分后，您将删除 nans，这会弄乱样本数。在拆分前执行data.dropna()。
3. 您通过传递(X_test, predictions) 错误地使用了model.score(X_test, y_test)。请改用 accuracy_score(X_test, predictions) 和这些参数，或修正语法。

from sklearn.tree import DecisionTreeRegressor #<---- FIRST ISSUE
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


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

data = data.dropna() #<--- SECOND ISSUE

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

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

model = DecisionTreeRegressor()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
score = accuracy_score(y_test, predictions) #<----- THIRD ISSUE
score