问题描述
我正在尝试使用graphviz可视化决策树，并在尝试绘制决策树时出现以下错误:
I am trying to visualize a decision tree using graphviz and while trying to plot the decision tree I am getting the below error :
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\tree\export.py in export_graphviz(decision_tree, out_file, max_depth, feature_names, class_names, label, filled, leaves_parallel, impurity, node_ids, proportion, rotate, rounded, special_characters, precision)
427 "does not match number of features, %d"
428 % (len(feature_names),
--> 429 decision_tree.n_features_))
430
431 # The depth of each node for plotting with 'leaf' option
ValueError: Length of feature_names, 225 does not match number of features,
208
我的代码
dt=DecisionTreeClassifier(class_weight="balanced", min_samples_leaf=30)
fit_decision=dt.fit(X_train_res,y_train_res)
from graphviz import Source
from sklearn import tree
Source( tree.export_graphviz(fit_decision, out_file=None, feature_names=data.columns))
你能告诉我哪里出了问题吗?
Can you tell me what went wrong?
推荐答案
您的data.columns包含所有features + label的名称，因为它没有分为X_train_res和y_train_res.您需要在X_train_res中传递要素名称，而不是data.columns来获得确切的要素，否则它将包括标签.在这种情况下，我假设X_train和y_train是从 data 派生的.
Your data.columns contains names of all the features+label since it is not divided into X_train_res and y_train_res. You need to pass the feature names in in X_train_res instead of data.columns to get the exact features else it will include the labels as well. I am assuming that X_train and y_train are derived from data in this case.
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