When should I use feature selection?
Top reasons to use feature selection are:
- It enables the machine learning algorithm to train faster.
- It reduces the complexity of a model and makes it easier to interpret.
- It improves the accuracy of a model if the right subset is chosen.
- It reduces overfitting.
What are the benefits of performing feature selection before modeling your data?
Three benefits of performing feature selection before modeling your data are:
- Reduces Overfitting: Less redundant data means less opportunity to make decisions based on noise.
- Improves Accuracy: Less misleading data means modeling accuracy improves.
- Reduces Training Time: Less data means that algorithms train faster.
How important is random forest Select feature?
Random Forests are often used for feature selection in a data science workflow. The reason is because the tree-based strategies used by random forests naturally ranks by how well they improve the purity of the node. Thus, by pruning trees below a particular node, we can create a subset of the most important features.
Should I split data before feature selection?
Actually, there is a contradiction of 2 facts that are the possible answers to the question: The conventional answer is to do it after splitting as there can be information leakage, if done before, from the Test-Set.
What are the features of random forest?
Features of Random Forests
- It is unexcelled in accuracy among current algorithms.
- It runs efficiently on large data bases.
- It can handle thousands of input variables without variable deletion.
- It gives estimates of what variables are important in the classification.
Is feature selection necessary when applying random forest classification?
When I try to perform random forest classification, I get very low accuracy such as 0.53. According to some resources, there is no need of feature selection when applying random forest because it is very powerful method, and it chooses most important features.
How accurate are random forests?
They are highly accurate. They generalize better. How does Random forest select features? Random forests consist of 4 –12 hundred decision trees, each of them built over a random extraction of the observations from the dataset and a random extraction of the features.
What happens if only training set is used for feature selection?
Secondly, if only Training Set is used for feature selection, then the test set may contain certain set of instances that defies/contradicts the feature selection done only on the Training Set as the overall historical data is not analyzed.
Is there a benchmark for feature selection?
Such a benchmark will guid you in the use of feature selection. You will need such guidance since there are many options (e.g., the number of features to select, the feature selection algorithm) an since the goal is usually the predication and not the feature selection so feedback is at least one step away. Share Improve this answer