Words are easy, like the wind;
Faithful friends are hard to find.
The uncountries are places that don't exist, but perhaps should. If you're starting your own country or are hoping to secede from your current employer (here's looking at you States of the US), you might find this list useful.
The list of uncountries is generated by training on list of 257 countries and territories.
Here's my bucket list of where I'm going next:
Below are the alphabetically first 4–10 letter single-word uncountries for each letter. In some cases, no names of a given length were generated for a given letter.
And below are uncountries that are composed of compound words. The neural network doesn't always do a good job in capitalization.
Here are all some lists with common suffixes
*nia Ariania Aruenia Bamenia Bolsnia Bukania Caminia Carenia Copania Eniania Eruinia Eryinia Eyuinia Fvounia Gapania Gorania Guyinia Imgania Lebania Lepania Mezania Pagonia Pamonia Piainia Pirania Saminia Sesinia Simania Somenia Sorinia Tinonia Turunia Urzenia Badetcinia Damalhania Denwarinia Inteniania Mangevinia Seregiania Tezadtinia Tudennenia Akinia Arenia Arunia Bocnia Boinia Bounia Buinia Burnia Byunia Caunia Eminia Gainia Geenia Geinia Giania Guania Guinia Guonia Gwinia Jhunia Jiinia Jirnia Kcenia Leinia Lornia Neenia Rernia Ruenia Sannia Shinia Siinia Siunia Suinia Uninia Vasnia Arefeonia Bevomania Dacucania Eziboonia Gibstania Klbininia Setrounia Shlatania Suunienia Teroninia EwDirireonia Aeirania Bemginia Bunyonia Canmania Carginia Carnania Cosrania Culiinia Cumiinia Duinania Ezupinia Geziania Guinenia Guurania Konvonia Lalzinia Lertania Marbania Nandania Narnania Nenconia Pastania Sadiania Sazcinia Sigwenia Smeminia Sonconia Surbania Taigonia Tebcania Tendania Unyrania Cania Conia Fania Henia Jania Jonia Kinia Lonia Mania Ninia Nonia Sania Tenia Tonia Vania
*lan Anualan Binelan Biselan Comelan Donolan Eduulan Iferlan Ilaslan Iudelan Papilan Potalan Srinlan Takilan Tamglan Cemuneilan Gehsyanlan Mecineslan Amurenoilan Aralan Cralan Geilan Inilan Innlan Kerlan Nanlan Sorlan Tnulan Beugeilan Condamlan Cunogslan Gantiulan Geevallan Gienyslan Memsinlan Mertorlan Minnaulan Mururolan Neminolan Sandeslan Sennerlan Titorilan Vertonlan Andenlan Betarlan Ceneslan Cunmelan Curislan Femanlan Geamilan Keberlan Larielan Meloelan Menrulan Molielan Otenelan Redallan SDatelan Selenlan Alan Glan Tlan Bolan Bulan Culan Galan Malan Selan Solan
*land Garland Hasland Ujoland Bandesland Benhelland Bhqlalland Dhinioland Lenkalland Macgalland Vuleslland Caland Feland Maland Saland Anderland Cemerland Geunoland Lutkaland Mowurland Panciland Parraland Anreland Asealand Hzuuland Maerland Masrland Memoland Namaland Navaland Ponoland Tuysland Vetaland
*ana Amynana Balpana Burgana Congana Fuubana Gainana Gaulana Guiiana Somuana Tartana Vehcana Cunheqrana Berniwhpana Antana Argana Buvana Mabana Merana Mobana Relana Rucana Semana Sikana Nteradana Gitanana Hana Lana Mana Sana Giana Guana Gvana Toana
*ica Cinuica Deyrica Goitica Maltica Mannica Merlica Peotica Raryica Sortica Stamica Sumhica Tektica Tiumica Utiuica Bemgbicica Aniica Bapica Narica Sanica Selica Sibica Gatuitica Iuperiica Ventalica Buuntica Bwentica Sorgeica Uica Baica Umica
*can Banecan Celican Jelican Pelecan Deslisacan Hatendacan Leucan Noccan Tircan Tlycan Shaylican Suniracan Cerarcan Emunecan Gepuucan Mamescan Salgican Vongican Ucan
*dan Euvadan Gtardan Monmdan Seundan Srisdan Unendan Banitisdan Ringkeldan Bildan Landan Saldan Soldan Sordan Tamdan Gakgasdan Mremaldan Stelosdan Lapardan Siwesdan Srunadan
*stan Baystan Caistan Velstan Gentiastan Getnicistan Naporrestan Gistan Mastan Tengastan Sinistan
*tar Lalatar Sanktar Simntar Somytar Swettar Temitar Burekertar Jartar Tantar Unitar Gornitar Satar
Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.
Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.
When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.
Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.
Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.
The artwork was created in collaboration with my colleagues at the Genome Sciences Center to celebrate the 5 year anniversary of the Personalized Oncogenomics Program (POG).
The Personal Oncogenomics Program (POG) is a collaborative research study including many BC Cancer Agency oncologists, pathologists and other clinicians along with Canada's Michael Smith Genome Sciences Centre with support from BC Cancer Foundation.
The aim of the program is to sequence, analyze and compare the genome of each patient's cancer—the entire DNA and RNA inside tumor cells— in order to understand what is enabling it to identify less toxic and more effective treatment options.
Principal component analysis (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.
To retain trend and patterns in the reduced representation, PCA finds linear combinations of canonical dimensions that maximize the variance of the projection of the data.
PCA is helpful in visualizing high-dimensional data and scatter plots based on 2-dimensional PCA can reveal clusters.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Principal component analysis. Nature Methods 14:641–642.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.
To achieve a `k` index for a movement you must perform `k` unbroken reps at `k`% 1RM.
The expected value for the `k` index is probably somewhere in the range of `k = 26` to `k=35`, with higher values progressively more difficult to achieve.
In my `k` index introduction article I provide detailed explanation, rep scheme table and WOD example.
The effect is intriguing and facetious—yes, those are real words.
But these are not: necronology, abobionalism, gabdologist, and nonerify.
These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.
And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.
And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.
Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.
When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.
And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.
An exploration of things that are missing in the human genome. The nullomers.
Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167