Flesch Kincaid Readability Tests
Data Skeptic19 Apr 2021

Flesch Kincaid Readability Tests

Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand?

While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease.

In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective.

For empirical validation of these metrics, the plot below compares English language Wikipedia pages with "Simple English" Wikipedia pages. The analysis Kyle describes in this episode yields the intuitively pleasing histogram below. It summarizes the distribution of Flesch reading ease scores for 1000 pages examined from both Wikipedias.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(601)

Video Recommendations in Industry

Video Recommendations in Industry

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...

26 Des 202538min

Eye Tracking in Recommender Systems

Eye Tracking in Recommender Systems

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...

18 Des 202552min

Cracking the Cold Start Problem

Cracking the Cold Start Problem

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...

8 Des 202539min

Designing Recommender Systems for Digital Humanities

Designing Recommender Systems for Digital Humanities

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te...

23 Nov 202536min

DataRec Library for Reproducible in Recommend Systems

DataRec Library for Reproducible in Recommend Systems

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea...

13 Nov 202532min

Shilling Attacks on Recommender Systems

Shilling Attacks on Recommender Systems

In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm...

5 Nov 202534min

Music Playlist Recommendations

Music Playlist Recommendations

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste...

29 Okt 202552min

Bypassing the Popularity Bias

Bypassing the Popularity Bias

15 Okt 202534min

Populært innen Vitenskap

fastlegen
tingenes-tilstand
jss
forskningno
rekommandert
rss-zahid-ali-hjelper-deg
rss-paradigmepodden
sinnsyn
vett-og-vitenskap-med-gaute-einevoll
rss-overskuddsliv
nordnorsk-historie
kvinnehelsepodden
tidlose-historier
villmarksliv
liberal-halvtime
rss-inn-til-kjernen-med-sunniva-rose
fjellsportpodden
grunnstoffene
nevropodden
rss-rekommandert