Remove 2011 Remove Metrics Remove Modeling Remove Testing
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Running Code and Failing Models

DataRobot

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you. But it is not routine.

Metrics 156
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Your Chance: Want to test an agile business intelligence solution? The term “agile” was originally conceived in 2011 as a software development methodology. Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. Finalize testing.

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How to Optimize Marketing and Sales Operations

Jedox

Typical metrics such as impressions, unique website visitors, raw and qualified leads, sales growth, sales target and target achievement, customer acquisition costs, customer churn rate, sales cycle length are among the ever-growing list of marketing metrics becoming commonly used. The evolution of marketing data.

Sales 95
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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.