A Hands on Guide to Automated Feature Engineering using Featuretools

#artificialintelligence

Anyone who has participated in machine learning hackathons and competitions can attest to how crucial feature engineering can be. It is often the difference between getting into the top 10 of the leaderboard and finishing outside the top 50! I have been a huge advocate of feature engineering ever since I realized it's immense potential. But it can be a slow and arduous process when done manually. I have to spend time brainstorming over what features to come up, and analyze their usability them from different angles.


The Hitchhiker's Guide to Feature Extraction

#artificialintelligence

Good Features are the backbone of any machine learning model. And good feature creation often needs domain knowledge, creativity, and lots of time. And some other ideas to think about feature creation. TLDR; this post is about useful feature engineering methods and tricks that I have learned and end up using often. Have you read about featuretools yet? If not, then you are going to be delighted.


The Hitchhiker's Guide to Feature Extraction

#artificialintelligence

Good Features are the backbone of any machine learning model. And good feature creation often needs domain knowledge, creativity, and lots of time. TLDR; this post is about useful feature engineering methods and tricks that I have learned and end up using often. Have you read about featuretools yet? If not, then you are going to be delighted.


Machine learning 2.0 : Engineering Data Driven AI Products

arXiv.org Artificial Intelligence

ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deployment - in favor of a rapid, 8-week process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs. This accomplishes what we call a "minimum viable data-driven model," delivering a ready-to-use machine learning model for problems that haven't been solved before using machine learning. We provide provisions for the refinement and adaptation of the "model," with strict enforcement and adherence to both the scaffolding/abstractions and the process. We imagine that this will bring forth the second phase in machine learning, in which discovery is subsumed by more targeted goals of delivery and impact.


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@machinelearnbot

Neural turing machines and GANs often don't train well, with heavy dependence on rand seed. Unlike robust random forests, require heavy feature tuning. All of these are problems worth thinking about (and researching more). Deep learning practitioners are extraordinarily talented and imaginative.