Visualizing decision tree with feature names The Next CEO of Stack Overflow2019 Community Moderator Electiondecision trees on mix of categorical and real value parametersVisualizing N-way frequency table as a Decision Tree in RDecision Tree generating leaves for only one caseOrdinal feature in decision treeInterpreting Decision Tree in context of feature importancespython sklearn decision tree classifier feature_importances_ with feature names when using continuous valuesa simple way to test wether a tree-based classifier would transfer well to a target population?Visualizing Decision Tree of K-Nearest-Neighbours classifierWhy do we need a gain ratioValue of features is zero in Decision tree Classifier
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Visualizing decision tree with feature names
The Next CEO of Stack Overflow2019 Community Moderator Electiondecision trees on mix of categorical and real value parametersVisualizing N-way frequency table as a Decision Tree in RDecision Tree generating leaves for only one caseOrdinal feature in decision treeInterpreting Decision Tree in context of feature importancespython sklearn decision tree classifier feature_importances_ with feature names when using continuous valuesa simple way to test wether a tree-based classifier would transfer well to a target population?Visualizing Decision Tree of K-Nearest-Neighbours classifierWhy do we need a gain ratioValue of features is zero in Decision tree Classifier
$begingroup$
from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()
X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.
Now i applied decision tree classifier on this model, i got this.
i took max_depth as 3 just for visualization purpose.
my question is i want to get feature names in my output instead of index as X2599, X4 etc.
i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.
visualization decision-trees
asked Mar 23 at 16:45
torBhakttorBhakt
1
$endgroup$
add a comment |
$begingroup$
from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()
X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.
Now i applied decision tree classifier on this model, i got this.
i took max_depth as 3 just for visualization purpose.
my question is i want to get feature names in my output instead of index as X2599, X4 etc.
i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.
visualization decision-trees
asked Mar 23 at 16:45
torBhakttorBhakt
1
$endgroup$
add a comment |
$begingroup$
from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()
X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.
Now i applied decision tree classifier on this model, i got this.
i took max_depth as 3 just for visualization purpose.
my question is i want to get feature names in my output instead of index as X2599, X4 etc.
i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.
visualization decision-trees
asked Mar 23 at 16:45
torBhakttorBhakt
1
$endgroup$
from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()
X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.
Now i applied decision tree classifier on this model, i got this.
i took max_depth as 3 just for visualization purpose.
my question is i want to get feature names in my output instead of index as X2599, X4 etc.
i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.
visualization decision-trees
visualization decision-trees
asked Mar 23 at 16:45
torBhakttorBhakt
1
asked Mar 23 at 16:45
torBhakttorBhakt
1
asked Mar 23 at 16:45
torBhakttorBhakt
1
asked Mar 23 at 16:45
torBhakttorBhakt
1
asked Mar 23 at 16:45
torBhakttorBhakt
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You can use graphviz instead. and use the following code to view the decision tree with feature names.
import pydotplus
import sklearn.tree as tree
from IPython.display import Image
dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot',
feature_names=dt_feature_names, class_names=dt_target_names,
filled=True)
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
This will display feature names with values, gini coefficient, sample, value and class
answered Mar 23 at 22:09
Cini09Cini09
166
$endgroup$
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
You can use graphviz instead. and use the following code to view the decision tree with feature names.
import pydotplus
import sklearn.tree as tree
from IPython.display import Image
dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot',
feature_names=dt_feature_names, class_names=dt_target_names,
filled=True)
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
This will display feature names with values, gini coefficient, sample, value and class
answered Mar 23 at 22:09
Cini09Cini09
166
$endgroup$
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
add a comment |
$begingroup$
You can use graphviz instead. and use the following code to view the decision tree with feature names.
import pydotplus
import sklearn.tree as tree
from IPython.display import Image
dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot',
feature_names=dt_feature_names, class_names=dt_target_names,
filled=True)
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
This will display feature names with values, gini coefficient, sample, value and class
answered Mar 23 at 22:09
Cini09Cini09
166
$endgroup$
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
add a comment |
$begingroup$
You can use graphviz instead. and use the following code to view the decision tree with feature names.
import pydotplus
import sklearn.tree as tree
from IPython.display import Image
dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot',
feature_names=dt_feature_names, class_names=dt_target_names,
filled=True)
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
This will display feature names with values, gini coefficient, sample, value and class
answered Mar 23 at 22:09
Cini09Cini09
166
$endgroup$
You can use graphviz instead. and use the following code to view the decision tree with feature names.
import pydotplus
import sklearn.tree as tree
from IPython.display import Image
dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot',
feature_names=dt_feature_names, class_names=dt_target_names,
filled=True)
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())
This will display feature names with values, gini coefficient, sample, value and class
answered Mar 23 at 22:09
Cini09Cini09
166
answered Mar 23 at 22:09
Cini09Cini09
166
answered Mar 23 at 22:09
Cini09Cini09
166
answered Mar 23 at 22:09
Cini09Cini09
166
166
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
add a comment |
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
$begingroup$
This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
$endgroup$
– torBhakt
Mar 24 at 10:57
add a comment |
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