O'Reilly on Data

MAY 27, 2020

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. The report has pages of careful caveats, but in the end it treats these risk-adjusted ratios as a good measure of a surgeon’s performance.

Risk 356

Risk Testing Measurement Reporting 356

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. ” “Data science” was first used as an independent discipline in 2001. Both data science and machine learning are used by data engineers and in almost every industry.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) from sklearn.neighbors import NearestNeighbors from random import randrange. return synthetic.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

JUNE 30, 2016

The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. In the presence of model misspecification, the estimator $hatpsi$ is inconsistent. the curse of dimensionality). These spaces are larger than $cal{F}_textrm{logistic}$ above.

Statistics 44

Statistics Optimization Modeling Experimentation 44