Building Explainable Models with Auger.AI

For some of you it’s not just the accuracy of the predictive model that is important, but how explainable it is. In this webinar, Vladislav shows how to limit your search to the most interpretable algorithms. He also shows how to use the Auger.AI user interface to provide more information on what drives the model

Timeseries Analysis with Auger.AI

Integrating Auger.AI Into Your Data Science Pipeline and Applications

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

Best Practices in Machine Learning Prediction

In this webinar, Vladyslav Khizhanov, discusses how to build optimal machine learning-based prediction models with our Auger.AI Automated Machine Learning Service. The following topics are covered in the discussion: Sizing your instances based on data size Choosing algorithms (i.e. restricting search) based on data dimensionality (rows and columns) and data distribution (edited) Restricting search based

EPL Fantasy Point Prediction using Auger.AI

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

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

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,