基于catogorical d的Sklearn树分类

Question

我试图创建一个简单的分类树分类疾病症状。我试过用sklearn树分类器。 它给出了以下错误。我的代码和错误都在那里。 有什么建议吗？在

import numpy as np
from sklearn import tree
symptoms = [['flat face','poor moro','hypotonia'],['small head','small jaw','overlapping fingers'], ['small eyes','cleft lip','cleft palate']]
lables = [['Trisomy 21'],['Trisomy 18'],['Trisomy 13']]
classify = tree.DecisionTreeClassifier()
classify = classify.fit(symptoms, lables)

它给出以下错误

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Answer 1

您需要使用label encoder来编码字符串值。以下内容可满足您的要求：

import numpy as np
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
symptoms = [['flat face','poor moro','hypotonia'],['small head','small jaw','overlapping fingers'], ['small eyes','cleft lip','cleft palate']]
lables = [['Trisomy 21'],['Trisomy 18'],['Trisomy 13']]
df = pd.concat([pd.DataFrame(symptoms), pd.DataFrame(lables)], axis=1)
x_cols = ['sym1', 'sym2', 'sym3']
y_col = 'target'
df.columns = x_cols + [y_col]
df = df.apply(LabelEncoder().fit_transform)
classify = tree.DecisionTreeClassifier()
classify.fit(df[x_cols].values, df[y_col].values)