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You asked: What are the top performing techniques used in predictive analytics?
Our answer: In our experience, regression models, decision trees and random forests are the most popular and powerful predictive analytics techniques.
Both linear and logistic regression are simple to understand, yet powerful techniques. Decision trees provide the additional benefit of being interpretable as a set of If-Then rules. If you are only focused on predictive performance rather than interpretability and want to spend less time on feature engineering as you’d need to do when using logistic regression, then random forests are the method of choice.
Throughout all of our benchmarking studies in credit scoring, marketing analytics and fraud detection, we have found that random forests consistently outperform the other predictive analytics techniques.