Tree-Based dimensionality reduction in DNN algorithms - neural-network

Question

My question is straightforward: is it possible to use a tree-based dimensionality reduction such as feature importance embedded in the Random Forest before training the dataset with a DNN algorithm?
In other words, does the use of tree-based feature importance prevents the use of training algorithms different from the tree/Random Forest?

Answer 1

I think you should read the DNN article.
Why? Why do you want to use Random Forest before DNN training?
Yes, you can display the feature importance of random-forest using
random_forest = RandomForestClassifier(random_state=42).fit(x_train, y_train)
feature_importances = DataFrame(random_forest.feature_importances_,
index = x_train.columns,
columns=['importance']).sort_values('importance',
ascending=False)
print(feature_importances)
But this is a feature-extraction method. The DNN is a neural-network method.
DNN is more complex than random-forest, while random-forest handles feature-extraction, DNN handles
feature-extraction,
back-propagation,
feed-forward methods.
If you feed enough training samples for DNN, you will have higher accuracy.
Does the use of tree-based feature importance prevents the use of training algorithms?
No, based on the problem, the sufficient feature size and samples vary. Usually, you don't use random-forest to extract 1M images feature importance.
Also, you don't use DNN for small-datasets.