Question

我正在尝试为以下数据集制作决策树：https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice

此数据集包含一些分类变量（例如，丈夫的职业：1,2,3,4）。当我创建决策树时，分类值基于“小于或大于”值进行拆分。换句话说，我的树中有一个节点按如下方式分割数据：“Occupation Husband＆lt; = 2.5”。如何调整此代码以便将分类变量考虑在内？当我打印'data.info（）'时，数据类型是正确的。

import pandas as pd
import numpy as np
import os

from sklearn import tree
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.model_selection import train_test_split

from matplotlib import pyplot as plt
import seaborn as sns

import graphviz
import pydotplus
import io
from scipy import misc

os.chdir("path") #path containing datacontra.csv file

data = pd.read_csv("datacontra.csv", dtype={'Age': np.float64, 'EduW':np.object, 'EduH':np.object, 'Child': np.int64, 'ReliW': np.object, 'WorkW':np.object, 'OccuH': np.object, 'SOLI': np.object, 'MediaExp': np.object, 'T':np.object})

data.describe()
data.head()
data.tail()

data.info()

train, test = train_test_split(data,test_size = 0.05)
print("Training size" + str(len(train)))
print("Test size " + str(len(test)))
train.shape

features = list(data.columns[:9])
label = list(data.columns[9])
print(list(data.columns[:9]))
print(list(data.columns[9]))

X_train = train[features]
print(X_train.shape)
y_train = train[label]
print(y_train.shape)

X_test= test[features]
y_test = test[label]

c = DecisionTreeClassifier()

dt = c.fit(X_train,y_train)

path = ("/Users/sabinekuypers/Documents/Charlotte 461/")
def show_tree(tree, features, path):
    f = io.StringIO()
    export_graphviz(tree, out_file=f, feature_names = features)
    pydotplus.graph_from_dot_data(f.getvalue()).write_png(path)
    img = misc.imread(path)
    plt.rcParams["figure.figsize"]=(20,20)
    plt.imshow(img)

show_tree(dt, features,'dt_tree.png')

y_pred = c.predict(X_test)
y_pred

from sklearn.metrics import accuracy_score

score = accuracy_score(y_test, y_pred)*100
print("Accuracy: ",round(score,1),"%")

提前谢谢

Answer 1

虽然决策树能够处理分类值，但在sklearn中，您必须对它们进行二进制编码。例如，您的功能Husband's Occupation [1,2,3,4]应该成为三个功能，每个功能都针对给定的职业值进行二进制编码。您可以在pd.get_dummies的pandas中执行此操作，如下所示：

occ_dummies = pd.get_dummies(df["OccuH"], drop_first=True)
data = pd.concat([data.drop("OccuH", axis=1), occ_dummies], axis=1)

从那里，您可以像以前一样继续使用您的数据。

我将就drop_first kwarg提出一点意见。使用它的原因是为了避免创建线性依赖关系，如One-hot vs dummy encoding in Scikit-learn中所述。

如何在Scikit Learn Decision Tree中基于分类变量拆分节点？

1 个答案: