[英]How to extract random forest tree rules for a Multiclass Classification?
嗨,我想在多類別分類的情況下從一棵樹中提取規則
from sklearn.tree import _tree
from sklearn.tree import DecisionTreeClassifier
#creat a gaussian classifier
clf=RandomForestClassifier(n_estimators=100)
#train the model using the training sets y_pred=clf.predict(X_test)
clf.fit(X_train,y_train)
#extract one tree from the forest
model = clf.estimators_[0]
def find_rules(tree,features):
dt=tree.tree_
def visitor(node,depth):
indent= ' ' * depth
if dt.feature[node] != _tree.TREE_UNDEFINED:
print('{} if <{}> <= {}:'.format(indent,features[node],round(dt.threshold[node],100)))
visitor(dt.children_left[node],depth+1)
print('{}else:'.format(indent))
visitor(dt.children_right[node],depth+1)
else:
print('{} return {}'.format(indent,dt.value[node]))
visitor(0,1)
find_rules(model, iris.feature_names)
請檢查以下代碼。 它似乎有效。 只有一個小變化
def find_rules(tree,features):
dt=tree.tree_
def visitor(node,depth):
indent= ' ' * depth
if dt.feature[node] != _tree.TREE_UNDEFINED:
print('{} if <{}> <= {}:'.format(indent,features[dt.feature[node]],round(dt.threshold[node],100)))
# in the previous line i added a backward-mapping
# for the feature id
visitor(dt.children_left[node],depth+1)
print('{} else:'.format(indent))
visitor(dt.children_right[node],depth+1)
else:
print('{} return {}'.format(indent,dt.value[node]))
visitor(0,1)
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