决策树分数总是返回1

Question

使用决策树训练时，我的测试集得到100％的分数，而且我认为这有点奇怪，因为我设置了max_depth = 2。我不明白我做错了。我在火车和测试集上将套装分开，但是分类器仍然返回1。这是我的代码。

这是我的数据集 songs.csv 。关于此数据集的一些信息：大小：400个元素，类的分布几乎是统一的，所以我不明白为什么决策树给出这样的理想得分，即使max_depth = 2。

import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score

df = pd.read_csv('D:Projects/datasets/songs.csv')

X = df.drop(['lyrics', 'song', 'artist'], axis=1)
y = df.artist

le = LabelEncoder()
le.fit(X.genre.unique())
X.genre = le.transform(X.genre)
le.fit(y.unique())
y = pd.Series(le.transform(y))

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.35)
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=2)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
accuracy_score(y_test, predictions)

Answer 1

也许问题来自数据集。我尝试了RandomForestClassifier，并获得了1.0精度。同样，一起使用验证和测试数据。 But the result was not changed. This is the tree graph.

>>> text_representation = tree.export_text(clf)
>>> print(text_representation)

|--- feature_1 <= 0.50
|   |--- class: 2
|--- feature_1 >  0.50
|   |--- feature_1 <= 1.50
|   |   |--- class: 1
|   |--- feature_1 >  1.50
|   |   |--- class: 0

这是我的完整代码：

import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import ensemble
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score

df = pd.read_csv('songs.csv')

X = df.drop(['lyrics', 'song', 'artist'], axis=1)
y = df.artist

le = LabelEncoder()
le.fit(X.genre.unique())
X.genre = le.transform(X.genre)
le.fit(y.unique())
y = pd.Series(le.transform(y))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2
    
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=2)
clf.fit(X_train, y_train)
predictions = clf.predict(X_val)
print(accuracy_score(y_val, predictions))

predictions = clf.predict(X_test)
print(accuracy_score(y_test, predictions))

text_representation = tree.export_text(clf)
print(text_representation)

clf = ensemble.RandomForestClassifier(criterion='entropy')
clf.fit(X_train, y_train)
predictions = clf.predict(X_val)
print(accuracy_score(y_val, predictions))

predictions = clf.predict(X_test)
print(accuracy_score(y_test, predictions))

Answer 2

我使用您提供的代码和数据集培训了一个决策树模型，它似乎可以按预期工作。获得这样的小数据集的完美分类得分并不罕见，而且似乎很简单，可以通过高度2的树完美地解决。代码似乎没有错。

您可以使用 tree.plot_tree ：

作为理智检查，我还观察到，当您将树的高度限制在1中时，模型的准确性小于70％。因此，我认为您提供的代码很好。