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Construct new features automatically to build better models, save time and avoid the mistakes that can occur in a manual process.
Memory problems are tough to diagnose and fix — maybe more so in Python. Read about how we identified and fixed a memory problem in EvalML, and pick up some best practices you can...
Ever get a little confused by these three terms that sound so similar? Let's get some clarity on these important concepts.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
Go on a guided tour of how EvalML automatically builds, optimizes and evaluates supervised machine learning pipelines.
Let's explore how Featuretools generates new features for use in machine learning — automatically, quickly and easily.
Introducing EvalML — an open-source library for automated machine learning (AutoML) and model understanding, written in Python.
Knowing which customers might churn is helpful, but uplift modeling can give you a new window into the nuances of customers' responses, among other applications.
How to build better training examples in a fraction of the time.
I'll give a shoutout in the subsequent Tackling Competitions Post to the person with the best score in the previous post as an added incentive!
To make it easier to understand how a feature was generated automatically, Featuretools now has the ability to graph the lineage of a feature.
Never made an analytical model, or don't have enough time to dedicate to learning statistics, data science, and programming... but you know the business and have questions to answ...