Author: Shub

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EPL Fantasy Point Prediction using Auger.AI

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A Taxonomy of Automated Machine Learning

AutoML is one of the most robust areas of innovation in applied machine learning. New products in this space from the likes of Google and new AI-focused startups are appearing constantly, all of which promise to make machine learning accessible to the masses without the need for trained data scientists. At its base, AutoML involves

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How Does AutoML Address Data Pre-processing?

Data preprocessing is an important aspect of automated machine learning, as generating a usable dataset for prediction and classification problems is among the most time-consuming aspects of data science problems. Most machine learning algorithms work only with well-structured data, but in reality, most real-world data needs considerable work prior to being usable. In this 30

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Power of Ensembles in Automated Machine Learning

Automated machine learning is making data science and machine learning accessible to more people. An emerging area of automated machine learning is ensemble generation, a process where multiple algorithms are combined automatically that, together, provide better results than each individual algorithm on its own. Many of the machine learning contests, such as those on Kaggle,

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How Does Auger.AI Provide Such Fast And Accurate AutoML?

In our last post, we pointed out that Auger outperforms the new Microsoft AutoML and other AutoML tools such as H2O and TPOT. Specifically, we took the OpenML datasets that Microsoft used to compare their AutoML with other tools, time-limited to one hour, and compared Auger’s predictive models (algorithms plus hyperparameters). Auger provided an average

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Auger.AI Outperforms Other AutoML tools Even On Their Own Chosen Datasets

If you’re a regular reader of this blog you probably know that Microsoft recently released their own AutoML SDK. We at Auger.AI were excited to see this development. It helps to validate and build awareness of this exciting new product category: Automated Machine Learning. And we love to see other products to compare our results to. Microsoft in their

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Of Grapes and Mushrooms: From AutoML to OptiML

Something I rarely tell people is that I am a dropout from UC Davis’ Ph.D. viticulture program. One of the first models we built in Wine Production (which encompassed both growing and later production of wine) was based on mostly exogenous variables not under grower control. Things like temperature and humidity at various points in

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Automated Machine Learning  —  Not Just for Experts Anymore

Since we launched Auger a few months ago, we found that some of the most significant benefits were to novice machine learning users. These users typically don’t have great instincts on what machine learning algorithms to use for their problem. They usually have applied some statistical method to perform basic prediction, such as linear regression

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“Auto-What?” — A Taxonomy Of Automated Machine Learning

AutoML is one of the most robust areas of innovation in applied machine learning. New products in this space from the likes of Google and new AI-focused startups (such as our own Auger.AI) are appearing constantly, all of which promise to make machine learning accessible to the masses without the need for trained data scientists. At

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Comparing Open Source AutoML Tools

Auger vs. H2O vs. TPOT On Sample Datasets We often get asked how Auger compares to other AutoML tools. Luckily in these days of open source tools this is possible to do in a way that can be validated and reproduced by other users. First let’s describe the choice of datasets. It was important that they

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