Cloudera

AUGUST 17, 2023

Machine Learning (ML) is at the heart of the boom in AI Applications, revolutionizing various domains. Passengers would need to trust that the manufacturing process was as rigorous as the design process. However, issues arise when authors that lack a rigorous process compile their code into machine language, aka binaries.

Software 71

Software Risk Manufacturing Finance 71

Cloudera

APRIL 6, 2022

This blog post was written by Elizabeth Howell, Ph.D But first it must pass a rigorous, months-long commissioning period to make sure that the data will get back to Earth properly. The process (in an ideal world) begins up in space, when the satellite makes decisions on board about what to send back to Earth.

Big Data 98

Big Data Machine Learning Insurance Data Processing 98

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Domino Data Lab

JANUARY 20, 2019

What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? and intuitively what this means is if I make this picture more like the concept or a little less like the concept, how much would the probability of zebra change? TCAV Github.

Testing 63

Testing Modeling Machine Learning Data Science 63

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. This renders measures like classification accuracy meaningless. Chawla et al.,

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

ln this post he describes where and how having “humans in the loop” in forecasting makes sense, and reflects on past failures and successes that have led him to this perspective. It also owns Google’s internal time series forecasting platform described in an earlier blog post. Our team does a lot of forecasting. So what to do?

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113