IBM Big Data Hub

FEBRUARY 8, 2024

It refers to a set of metrics used to measure an organization’s environmental and social impact and has become increasingly important in investment decision-making over the years. In response, asset managers began to develop ESG strategies and metrics to measure the environmental and social impact of their investments.

Measurement 80

Measurement Metrics Reporting Strategy 80

Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. In their 2002 paper Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Alation

APRIL 12, 2022

In 2002, Capital One became the first company to appoint a Chief Data Officer (CDO). On the other, a data free-for-all creates risk—not only of data leaks with regulatory penalties, but also of inefficiency, as the same work and assets may be duplicated across the organization. Protecting Sensitive Data.

Data Quality 52

Data Quality Data Governance Metrics Data Strategy 52

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. 16, 1 (January 2002), 321–357. [3] from imblearn.over_sampling import SMOTE.

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