在我的 DecisionTree 模型上獲得 100% 的准確性

Question

這是我的代碼，無論測試規模有多大，它始終返回 100% 的准確率。 我使用了 train_test_split 方法，所以我認為不應該有任何重復的數據。 有人可以檢查我的代碼嗎？

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

編輯：自從我發現多個問題以來，我已經重新設計了我的答案。 請復制粘貼以下代碼以確保不留下任何錯誤。

問題 -

1. 您正在使用DecisionTreeClassifier而不是DecisionTreeRegressor來解決回歸問題。
2. 在進行測試訓練拆分后，您正在刪除nans ，這會弄亂樣本數量。 在拆分之前執行data.dropna() 。
3. 您通過傳遞它(X_test, predictions)錯誤地使用了model.score(X_test, y_test) (X_test, predictions) 。 請使用帶有這些參數的accuracy_score(X_test, predictions)代替，或修復語法。

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