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Machine Learning for Automated Data Science Tools


For the past five years, a team of research scientist at MIT’s Laboratory for Information and Decision Systems has been trying to create automation tools that enable the subject matter experts to use ML. The team first divided the process into a discrete set of steps. For example, the first step involved searching for buried patterns with predictive power, known as “feature selection engineering.” Second is called "model selection," in which the best modeling technique is chosen from the many available options. These steps are automated; releasing open-source tools to help domain experts efficiently complete them. Then these automated tools are grouped together, turning raw data into a trustworthy, conveyable model over the chain of seven steps. This chain of automation makes it possible for subject matter experts even those without data science experience to use machine learning to solve business problems.

Through machine learning for automated data science tools, ML 2.0 frees up subject matter experts to spend more time on the steps that truly require their discipline expertise, like deciding which problems to solve in the first place and evaluating how predictions impact business conclusion. The first model was built by the team to predict the performance of software projects against a host of delivery metrics. The model was found to be predicting correctly more than 80 percent of project performance outcomes after the testing was completed.

Using feature selection tools which involved a series of human-machine interactions, first recommended 40,000 features to the domain experts. At first, the humans used their aptness to narrow this list down to the 100 most promising features, and then they put to work training the machine-learning algorithm. Second, the domain experts used the software to counterfeit using the model, and to test how well it would work as new, real-time data came in. This method also extends the "train-test-validate" obligation typical to current machine-learning research, making it more applicable to real-world use. The model was then extended to make predictions for hundreds of projects on a weekly basis.

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