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I've this code in order to visualize the most important feature of each model:

dtc = DecisionTreeClassifier(min_samples_split=7, random_state=111)
rfc = RandomForestClassifier(n_estimators=31, random_state=111)
trained_model = dtc.fit(features_train, labels_train)
trained_model.fit(features_train, labels_train)
predictions = trained_model.predict(features_test)
importances = trained_model.feature_importances_
    std = np.std([trained_model.feature_importances_ for trained_model in 
 trained_model.estimators_], axis=0)
 indices = np.argsort(importances)[::-1]
    for f in range(features_train.shape[1]):
        print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
        plt.figure()
        plt.title("Feature importances")
        plt.bar(range(features_train.shape[1]), importances[indices], color="r", yerr=std[indices], align="center")
        plt.xticks(range(features_train.shape[1]), indices)
        plt.xlim([-1, features_train.shape[1]])
        plt.show()

Using RandomForestClassifier this code runs good but when I try it using Decison Trees classifier I get the following error:

std = np.std([trained_model.feature_importances_ for trained_model in trained_model.estimators_], axis=0)

builtins.AttributeError: 'DecisionTreeClassifier' object has no attribute 'estimators_'

Which attribute should I use see the most important feature of each model?

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Do Checkout this Link

To Visualise The Tree Itself

from sklearn.tree import export_graphviz
import graphviz

export_graphviz(tree, out_file="mytree.dot")
with open("mytree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph)

OR

from sklearn.tree import convert_to_graphviz
convert_to_graphviz(tree)

OR

from sklearn.tree import convert_to_graphviz
import graphviz

graphviz.Source(export_graphviz(tree))

The Visualisation You Can Get Will be Whole Tree Itself.. RF

To Display Feature Importances

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier.fit(features, labels)
for name, importance in zip(features.columns, classifier.feature_importances_):
    print(name, importance)
    ## Now You Can Do Whatever You Want(plot them using a Barplot etc)
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