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Words are easy, like the wind;

Faithful friends are hard to find.

—William Shakespeare

This is the biggest list—887,716 English *unwords*, generated from a list of 479,000 list of English words.

Medically compelling words, defined by some interesting suffixes (e.g. phobia), are presented in the *undiseases* section.

The list of *unwords* is filtered to exclude any hyphenation and apostrophes. I've made no attempt to stem the words—for example, to remove what appear to be plural forms.

baf

cah

daf

ebz

fal

gaa

hea

ibb

jaa

kaa

laa

maq

nai

oaa

paa

qqu

raz

sbz

tah

uea

vae

wel

xni

yab

zaa

bace

caak

dabn

eacc

faha

gaag

hadr

ibal

jabo

kaah

laep

maal

naan

oaah

paan

qeit

raef

sabm

taak

ubed

vace

wanr

xake

yada

zaac

babne

caang

dabna

eable

fadus

gaaba

habwe

iaced

jaacm

kaaab

ladde

maaco

naars

oaads

paabs

qeisa

radlo

sabil

tabal

udsel

vaard

wance

xibne

yebon

zaach

babold

caabah

dabnar

eables

facont

gaaada

habway

iacect

jaaday

kaadey

laclic

maakin

nabens

oaagee

paabay

quaben

raldos

sabane

taaque

udnygs

vabara

wamfly

xennis

yeamyl

zaaced

bacadil

caamale

daajarg

eabemic

facomas

gaabags

habwest

iacects

jaabate

kabadis

laclics

maakite

naaquan

oaalake

paaband

qeitage

raemony

sabanal

tabater

ubecide

vabitat

walcama

xeaiate

yazoris

zaacure

bacacree

caamahoo

dabnasas

eabidion

facomate

gaabacte

habwoofs

iaccuate

jaabarly

kabalion

lacloses

maaglive

nabaneer

oaaldiza

paabands

qeinator

ramaness

sabaless

tabakton

uangorin

vabipans

wameless

xeywords

ydneress

zaachner

bacaiting

caambaria

dabnaring

eablishes

facomania

gaaadedia

habwuiter

iacectium

jaadneway

kaahajout

laclidian

maacchial

nabaneest

oabarfort

paachting

qeinatory

raemoning

sabalaryl

taabotzle

udnyglyph

vabippary

wandwares

xendomate

yeesonian

zaachnick

bacaitings

caamahooed

daakarillo

eabialysis

facolatine

gaabustail

hacchotest

iabiaceous

jaabarness

kaagnation

lacloparia

maagillism

naantalous

oaadrooter

paacrasses

qturegrasm

raemonetts

sabalidase

tabiricate

uaragrable

vabipagean

wandwatark

xickinfert

yebonesses

zaacricker

bachplasted

caamahooing

dabnationer

eabidiology

facoscophic

gaablerment

hacchotized

ibbicarized

jaacoughter

kabstomment

lacloparian

maagilineal

nabionmaker

oabatrophic

paalligeret

quacidioned

raemonetted

sabalidated

tabisteculi

ubedability

vabipparies

wandwauning

yennishness

zaadishness

bachplasters

caamatoscope

dabnariolate

eablansmidal

facolubility

gaablikeness

hadeconwight

iaceulineson

jabification

kabheseeding

lacluntation

maachanavite

nabionership

oabatrophoic

paalodochord

quacefulness

raemonetting

sabalidation

tabjaceously

udnygromissi

vabippareism

wannachanced

yepenousness

zaachnickesh

bachanoloical

caamatoscopia

dabnationency

eablamanchium

facontagonite

gaaldrimahedy

hadiaboroness

iachomaniasis

jabimarometer

kabromedizone

lacrusization

maagilinolity

nabitariously

oacantraceous

paanolypaline

quaculiferous

rambreaulowed

sabanucelican

tabjaceousies

uemanociation

vabipparyment

wannachancing

xickingerline

yeryllinemism

zenbhidievine

bacecification

caamatoscopian

dabnationistic

eacenfigenised

facolubilities

gaarolatically

hacchotization

ibbicarization

jabimarosacily

kaihowfuklench

ladoculsionism

maakursismally

nactualization

oachuloglosynt

paalojiglactry

qansinquirosis

raemoologition

sabracoethygen

tabjaceousness

udnygromoidium

vaceheartedner

wannefictingly

yepfyrotarical

zalaedritellay

badainderedness

caanomaticnatic

dabstologically

eablaneonareous

facophthillahid

gaannificalpous

harrigonishment

ibbicarizations

jaberotherapeus

kabaelesiculous

lamphosescenter

maakuristically

nadastifferling

oachanosophagia

paaminguasechos

quaddractionism

rastarizingness

sabridaboletric

tacdicalization

uemalameisarian

vaceheartedness

wanenolaphonous

ynderapodometer

bakoographically

cabalothpization

dactalisingulate

eachematocarpous

fahfoiestheskule

gactiometrically

hadiaboastowning

ibbiographically

jabimoatalophesy

kawaddauriaceous

lamolatrological

maarographically

nadarogenesomacy

ochydramodenomic

paalodorrhaphrin

quaddractimation

rameocrasiferous

saconopasinectic

tachocroytograph

uemanobiological

vegonsculonizing

wandwatalvenwood

yermacographical

baeloazronahumian

cabritometrically

dabsomelscescence

eaticurlegittrial

failonesheeptress

gaarozooenciously

haudacycocetolite

iceiomorphization

jabufedereschilde

kawcayanganasasim

lamphophoraldumal

mabilioistarthine

naniquinpuffyaire

oachryptostecrous

pablinopolization

reaptillification

sabucetarculation

tachyphoschistomy

uemalodeativeness

vegonsculvinalize

whimsellepsitrify

zhonoussitization

bagoquincification

caccopstherization

dalatosephostomies

eacenfiginembiasis

fantrofingerniness

gaeragotisalgialis

hecalocorpossomies

icemohydrosemuline

jacunceuralization

kaitcholenthythris

leitocartronograph

mabersanthropalous

necrotimoperitoric

oachryptosphorenal

padlandfactiveness

quebunouscephalous

rearmidephenolitic

secrotomyzographic

tachycardometyrous

uenitanisticalness

vellheolinguachure

wanpimmenabilities

yermahvoacetoscoma

bahandaclionishness

cacethcoalyscraphic

dalanaphorrhachehic

eccolatibidological

fansomosacsimatical

gadiophoressessiric

hadiaboastripangous

icancraticulosality

kelocericostosthene

leliarystallization

mabersanthrological

nercrodyomeberation

odersotenographeral

palanreoncounterite

reconsosentializing

semidezalomolegneal

tagatomotheremotean

unberwrightenedness

velciarcomaryphitis

wenigrassiprulation

bedaginettaceousness

cachofigarpossometer

dactalismandolitrate

eachtrandrologistric

fedophosphotological

ganroldinocontonosus

henromycorrhhosoidal

iceiolarchythmiamoad

jabimoamordohechmood

kedipercustitalities

macalcromanchamiency

nondenchistentiation

ochyolorenentontagen

palnareoblothiameric

quebrobiogrhecartise

rerepervimestination

scartiometricolastic

talmacoparphocentone

undiffosoliopenazing

velluquithoreterical

bemicoloblacontomatic

cachoegnscophystomies

dalificationlessorate

eachygasonaryocarcour

fedorographistrolisan

gadicoarchioscopelous

hedetotypolorphograph

incentularsomoterasia

madychopheniaccuronal

nodaterationalization

octudioflorouphotopia

pechovobbintropnonaic

rechmodechmometrogamo

sabrilostehyricullite

tartosophazoloopellin

undyltracerbandenesol

vereurocerbordohycent

begipothranmaceraceous

caconsochnecoloscephal

danduocephalomosomeric

egyperyphopothermosate

frachipothostereinitis

gasciocatiosocrasilian

indaaldesitrehenalgian

malcuptisemothanculous

nercroplerographically

oecraumatressesthenibe

peaslothecamonometanic

quebrobiogrhecaryminal

reflesimomolithrosilic

secrotopoidontromastic

tatinochlogacyphologue

undongrensibiliatlemen

bembenocolbogistromatal

caciceptopomethyleneous

decletopyrrachystolmial

eiyandsceprinainization

galuardicolaristrolcoma

icetoplexistrosthhrenia

lucarucorynchomazintrin

macnchydrogeneumoseable

nonertertabolialization

orchopyrimosalgarositis

pantancoloideculaminone

tanacondrachonomethanic

We discuss the many ways in which analysis can be confounded when data has a large number of dimensions (variables). Collectively, these are called the "curses of dimensionality".

Some of these are unintuitive, such as the fact that the volume of the hypersphere increases and then shrinks beyond about 7 dimensions, while the volume of the hypercube always increases. This means that high-dimensional space is "mostly corners" and the distance between points increases greatly with dimension. This has consequences on correlation and classification.

Altman, N. & Krzywinski, M. (2018) Points of significance: Curse(s) of dimensionality *Nature Methods* **15**:399–400.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

Celebrate `\pi` Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day, 2016 `\pi` Day and 2017 `\pi` Day.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.