How to find feature importance for each class in multiclass classification

Question

I have written code to find the importance of each feature in the entire dataset for multiclass classification. Now I want to find feature importance for each class in multiclass classification, ie I want to find the list of features (for each class) that are more important to classify that individual classes.

from sklearn.datasets import make_classification
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
model = DecisionTreeClassifier()

model.fit(x3, y3)

importance = model.feature_importances_

for i,v in enumerate(importance):
    print('Feature[%0d]:%s, Score: %.6f' % (i,df.columns[i],v))
    
plt.subplots(figsize=(15,7))
plt.bar([x for x in range(len(importance))], importance)
plt.xlabel('Feature index')
plt.ylabel('Feature importance score')
plt.xticks(rotation=90)
plt.xticks(np.arange(0,len(df.columns)-2, 2.0))
plt.show()

EDIT (28-04-2022):

I read a paper titled Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization ; quoting:

On the evaluate section, we fist extract the 80 traffic features from the dataset and clarify the best short feature set to detect each attack family using RandomForestRegressor algorithm. Afterwards, we examine the performance and accuracy of the selected features with seven common machine learning algorithms.

Can anyone explain how this is done? click for picture from that paper

Answer 1

The decision trees are split into nodes that maximise information gain. Each split is based on the Gini index or entropy values. So the only way I think what you want to do can be achieved is by printing out the tree and examining it yourself visually, provided there are not too many nodes.

You can't say with certainty that one of your features is very important in discriminating against a certain class because suppose you have two classes, A and B. The feature that discriminates class A against class B is also discriminating class B against class A. So the importance of that feature is for both classes. In general, you can only get the overall feature importance not specific to any of your classes but the features that help get the work done.

Trees are highly unstable, and a slight change in your dataset will build an entirely new different tree from the first.

EDIT(28-04-2022):

The paper says they used Random-ForestRegressor, different from the decision tree you used. Random-ForestRegressor meant they had a regression task. The paper used the algorithm as a feature selection technique to reduce the 80 features. The few features selected (based on feature importance) were then used to train seven other different models. Using fewer features instead of the whole 80 will make the resulting models more elegant and less prone to overfitting.

It is important to know that Random forest is an ensemble method and has a lot of random happenings in the background such as bagging and bootstrapping. Feature importance is a form of model interpretation. It is difficult to interpret Ensemble algorithms the way you have described. Such a way would be too detailed. So, definitely, what they wrote in the paper is different from what you think.

Decision trees are a lot more interpretable. If you want to understand causality in your decision tree model, you can click here to see how the model can be converted into rules or as suggested earlier, observe the tree with your naked eyes.