![]() Hall created the ‘Cultural Iceberg Model’ as a way to explain cultural differences. Renowned psychoanalyst Sigmund Freud used an iceberg metaphor to describe the conscious, preconscious, and unconscious mind. The iceberg metaphor has long been used to differentiate between surface and lesser-known knowledge. The highly active r/IcebergCharts and a handy breakdown of what goes where on an iceberg chartĪlthough the Iceberg meme really became popular in early 2021, the meme has much older and academic origins. Literally, you can make an iceberg chart on anything, even on iceberg charts themselves! Icebergs can cover anything from conspiracy theories to video game lore. The chart is split into tiers, with the upper tiers covering the most well-known information and the lower tiers containing the more obscure or weirder information. “usually an image of an iceberg, captioned humorously so as to convey that the tip of the iceberg is the summation of the knowledge of most people, while the much larger submerged part of the iceberg is the sum of all knowledge of a particular topic.” ![]() Often focusing on creepy topics such as conspiracy theories, horror movies, and the weirder sides of the internet, the Iceberg chart has become an all-new way to categorize theories, movies, and other media.įirst of all, what exactly is an iceberg chart? According to the subreddit r/IcebergCharts, an iceberg chart is: Between late 2020 and early 2021, the trend blew up, expanding from a niche Reddit community to YouTube, where YouTubers made their name narrating and talking their viewers through each tier. At the beginning of this year, a new trend broke it big on the Internet: Iceberg charts. ![]()
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![]() It has relatively little to say about the prospects for our discovering such civilizations (or their discovering us) and much more to say about what happens once we do make contact, with a strong multidisciplinary approach. ![]() This is a collection of papers gathered under three large themes involved in contact with extraterrestrial civilizations. " - ForteanTimes - Dieser Text bezieht sich auf eine andere Ausgabe: paperback. a fascinating collection of essays examining how humanity might react to extraterrestrials.While is academically rigorous, it's also remains an essential introduction for anyone interested in SETI, xenobiology and UFOs. Civilizations Beyond Earth starts to redress the balance, edited skillfully by Douglas Vakoch, the only sociologist on staff at the SETI Institute in California, and Albert Harrison, a psychologist from the University of California. " For years sections of the SETI community have bemoaned the fact that the social sciences are often sidelined in favour of the hard sciences when it comes to SETI discussion. "At a time when new planets are being discovered around other stars at an unprecedented rate, this collection provides a much needed guide to the human impact of discovering we are not alone in the universe." - International Journal of Anthropology At a time when new planets are being discovered around other stars at an unprecedented rate, this collection provides a much needed guide to the human impact of discovering we are not alone in the universe. Scholars from such diverse disciplines as mathematics, chemistry, journalism, and religious studies offer innovative solutions for bridging the cultural gap between human and extraterrestrial civilizations, while recognizing the tremendous challenges of communicating at interstellar distances. Sociologists present the latest findings of novel surveys, tapping into the public’s attitudes about life beyond Earth to show how religion and education influence beliefs about extraterrestrials. Archaeologists and astronomers explore the likelihood that extraterrestrial intelligence exists, using scientific insights to estimate such elusive factors as the longevity of technological societies. If they make contact with an advanced alien civilization, how will humankind respond? In thinking about first contact, the contributors to this volume present new empirical and theoretical research on the societal dimensions of the Search for Extraterrestrial Intelligence (SETI). Astronomers around the world are pointing their telescopes toward the heavens, searching for signs of intelligent life. ![]() ![]() ![]() LRM1 and calculated accuracy which was seems to be okay. > Now I have created a model using Logistic regression i.e. > I have the data set and randomly samples test and train (in 30:70 ratio). My question is :What is the next step after doing the cross validation ? In Chapter 8 ‘Implementation of Near-Infrared Technology’ (pages 145 to 169) by P. Paul, MN.Ī second addition of that handbook was published in 2004. Pages 143-167 in: Near Infrared Technology in the Agriculture and Food Industries. Williams, PC (1987) Variables affecting near-infrared reflectance spectroscopic analysis. Is one of these theoretical more correct than the other? Should we use regression of true on predicted values, or vice versa. I would appreciate comments on the use of RPD in evaluation of prediction models. TRAIN CARET FULLWilliams, PC (1987) presents a table with the following interpretations for various RPD values (see full reference belowe):īased on this my prediction model with RPD=1.1 is very poor. The correlation coefficient between y-predicted and y-true is 0.43 RMSEP=19.84 Regression coefficient of y-true on y-predicted = 0.854 Standar deviation of y-true SD=21.94, and RPD = SD/RMSEP=1.10. I have performed a Leave One Out Cross Validation test using a dataset with102 y dependent=true) and x (explanatory) variables/records. Package ‘later’ is not available (for R version 3.5.0) So then I did install.packages("later") and the error I got was: TRAIN CARET INSTALLHow can package klaR be required to install itself? Anyway, I just went ahead and did library(klaR) and the end of these messages were:Įrror: package or namespace load failed for ‘klaR’ in loadNamespace(j <- i], c(lib.loc. Then it installs a bunch of other dependencies. ![]() Warning: dependency ‘later’ is not available > train_control model <- train(emotion~., data=tweet_p1, trControl=train_control, method="nb")ġ package is needed for this model and is not installed. 5, 1), lambda=c(.1, 1, 10))Ĭan caret extract predictions on each of the 5 test fold partitions with the best fitting model w/ optimal alpha & lambda values obtained via 10-fold CV? ![]() Train_control <- trainControl( method="cv", # Train elastic net logistic regression via 10-fold CV on each of 5 training folds using index argument. # Create levels yes/no to make sure the the classprobs get a correct name. ![]() How would you obtain the best fit model predictions on each of the 5 test fold partitions?įor example, using the following dataset: I’m working on a project with the caret package where I first partition my data into 5 CV folds, then train competing models on each of the 5 training folds with 10-fold CV and score the remaining test folds to evaluate performance. Precision or positive predictive value (PPV) Here is a wikipedia article that shows the formulas for calculating the relevant measures TRAIN CARET HOW TO> confusionMatrix(predictions, iris$Species)įinally! A clear post on how to do cross validation for machine learning in R! The final values used for the model were fL = 0 and usekernel = FALSE. Tuning parameter ‘fL’ was held constant at a value of 0Īccuracy was used to select the optimal model using the largest value. Resampling results across tuning parameters: Resampling: Cross-Validated (10 fold, repeated 3 times) So my question is which result should be used as the capability of the model?ģ classes: ‘setosa’, ‘versicolor’, ‘virginica’ 0.998 for model and confusionMatrix, respectively. In my real data this difference is larger 0.931 vs. In contrast, when I look at the result of the confusionMatris() function, accuracy is 0.96 (see below). TRAIN CARET CODEWhen I run your code of Repeated k-fold Cross Validation, and look at the content of the “model” variable, I get the following result with accuracy indicated as 0.9533333. © 2022 The Cincinnati Bengals.Thanks for your reply, Jason. ![]() ![]() ![]()
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