English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This note uses a conformal prediction procedure to provide further support onseveral points discussed by Professor Efron (Efron, 2020) concerningprediction, estimation and IID assumption. It aims to convey the followingmessages: (1) Under the IID (e.g., random split of training and testing datasets) assumption, prediction is indeed an easier task than estimation, sinceprediction has a 'homeostasis property' in this case -- Even if the model usedfor learning is completely wrong, the prediction results maintain valid. (2) Ifthe IID assumption is violated (e.g., a targeted prediction on specificindividuals), the homeostasis property is often disrupted and the predictionresults under a wrong model are usually invalid. (3) Better model estimationtypically leads to more accurate prediction in both IID and non-IID cases. Goodmodeling and estimation practices are important and, in many times, crucial forobtaining good prediction results. The discussion also provides one explanationwhy the deep learning method works so well in academic exercises (withexperiments set up by randomly splitting the entire data into training andtesting data sets), but fails to deliver many `killer applications' in realworld applications.

Homeostasis phenomenon in predictive inference when using a wrong learning model: a tale of random split of data into training and test sets