AWS Big Data

JANUARY 22, 2024

They classified the metrics and indicators in the following categories: Data usage – A clear understanding of who is consuming what data source, materialized with a mapping of consumers and producers. The success of the implementation meant assessing various aspects of the data infrastructure, data management, and business outcomes.

Data-driven 85

Data-driven Advertising Metadata Data Architecture 85

Cloudera

AUGUST 6, 2018

This is important because unlike diabetes or high blood pressure we don’t yet have clear metrics for Parkinson’s. In 2012 , only 25 of the region’s 48 countries had conducted at least two surveys over the past decade to track poverty. Indeed, as Stefaan Verhulst, co-founder of GovLab, says : “An increased use of data is not risk-free.

Machine Learning 50

Machine Learning Internet of Things Cost-Benefit Data-driven 50

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. from sklearn import metrics. from imblearn.over_sampling import SMOTE. from datetime import datetime. 0.01, 0.001] }.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

JUNE 2, 2019

Trying to dissect a model to divine an interpretation of its results is a good way to throw away much of the crucial information – especially about non-automated inputs and decisions going into our workflows – that will be required to mitigate existential risk. For kicks, try calculating this kind of metric within your own organization.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Looking at metrics of interest computed over subpopulations of large data sets, then trying to make sense of those differences, is an often recommended practice (even on this very blog).

Experimentation 61

Experimentation Testing Statistics Metrics 61

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? While image data has been the stalwart for deep learning use cases since the proverbial “ AlexNet moment ” in 2011-2012, and a renaissance in NLP over the past 2-3 years has accelerated emphasis on text use cases, we note that structured data is at the top of the list in enterprise.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

The Unofficial Google Data Science Blog

JUNE 30, 2016

Choose the weights $alpha$ that minimize the cross-validated risk: $hatalpha =argmin_{alpha} frac{1}{J}sum_{j=1}^Jfrac{1}{|mathcal V_j|}sum_{iin mathcal V_j} L(M_i, hat e_{alpha, mathcal T_j})$ subject to $quad 0 leq alpha_kleq 1, sum_{k=1}^Kalpha_k=1,$ and define the final estimator as $hat e_{hatalpha}(x)$. 2012): 25-46.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

MAY 31, 2016

This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. In other words, iROAS is the slope of a curve of the response metric plotted against the underlying advertising spend. They are non-overlapping geo-targetable regions.

Advertising 42

Advertising Testing Sales Statistics 42

CIO Business Intelligence

AUGUST 28, 2023

The company was quick to kick off its cloud journey in 2012, recognizing the need to scale seamlessly, get new services to market quickly and give staff and customers a great digital experience. Once identified, they’re ranked in order of risk so the team can focus on the most critical incidents first.

Cost-Benefit 78

Cost-Benefit Insurance Risk Management 78

Domino Data Lab

JULY 22, 2019

I went to a meeting at Starbucks with the founder of Alation right before they launched in 2012, drawing on the proverbial back-of-the-napkin. What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109