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How to be prepared for the coming Altcoin bull market.
So you bought some altcoins this fall, hoping to catch the next wave of 5, 10 even 100x+ returns like the ones we we saw earlier this year. If so, you are not alone. The return of the so called “alt…
altcoin  cryptocurrency  markets  analysis  bitcoin  compare  future  prediction  money 
4 days ago by orlin
[1706.02744] Avoiding Discrimination through Causal Reasoning
"Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively.
"Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them."
to:NB  to_read  causality  algorithmic_fairness  prediction  machine_learning  janzing.dominik  re:ADAfaEPoV  via:arsyed 
9 days ago by cshalizi
Welcome to Apache PredictionIO™!
Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you:

quickly build and deploy an engine as a web service on production with customizable templates;
respond to dynamic queries in real-time once deployed as a web service;
evaluate and tune multiple engine variants systematically;
unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics;
speed up machine learning modeling with systematic processes and pre-built evaluation measures;
support machine learning and data processing libraries such as Spark MLLib and OpenNLP;
implement your own machine learning models and seamlessly incorporate them into your engine;
simplify data infrastructure management.
opensource  ai  machinelearning  prediction  apache  spark  mllib  hbase  spray 
11 days ago by wjy
Statistical Thinking: Classification vs. Prediction
A special problem with classifiers illustrates an important issue.  Users of machine classifiers know that a highly imbalanced sample with regard to a binary outcome variable Y results in a strange classifier.  For example, if the sample has 1000 diseased patients and 1,000,000 non-diseased patients, the best classifier may classify everyone as non-diseased; you will be correct 0.999 of the time.  For this reason the odd practice of subsampling the controls is used in an attempt to balance the frequencies and get some variation that will lead to sensible looking classifiers (users of regression models would never exclude good data to get an answer).  Then they have to, in some ill-defined way, construct the classifier to make up for biasing the sample.  It is simply the case that a classifier trained to a 1/1000 prevalence situation will not be applicable to a population with a vastly different prevalence.  The classifier would have to be re-trained on the new sample, and the patterns detected may change greatly.  Logistic regression on the other hand elegantly handles this situation by either (1) having as predictors the variables that made the prevalence so low, or (2) recalibrating the intercept (only) for another dataset with much higher prevalence.  Classifiers' extreme dependence on prevalence may be enough to make some researchers always use probability estimators instead. One could go so far as to say that classifiers should not be used at all when there is little variation in the outcome variable, and that only tendencies should be modeled.
philosophy-of-engineering  classification  statistics  machine-learning  prediction  to-write-about  engineering-criticism 
12 days ago by Vaguery
Indiana Jones, Economist?! - Marginal REVOLUTION
In a stunningly original paper Gojko Barjamovic, Thomas Chaney, Kerem A. Coşar, and Ali Hortaçsu use the gravity model of trade to infer the location of lost cities from Bronze age Assyria! The simplest gravity model makes predictions about trade flows based on the sizes of cities and the distances between them. More complicated models add costs based on geographic barriers. The authors have data from ancient texts on trade flows between all the cities, they know the locations of some of the cities, and they know the geography of the region. Using this data they can invert the gravity model and, triangulating from the known cities, find the lost cities that would best “fit” the model. In other words, by assuming the model is true the authors can predict where the lost cities should be located. To test the idea the authors pretend that some known cities are lost and amazingly the model is able to accurately rediscover those cities.
econotariat  marginal-rev  commentary  study  summary  economics  broad-econ  cliometrics  interdisciplinary  letters  history  antiquity  MENA  urban  geography  models  prediction  archaeology  trade  trivia  cocktail  links  cool  tricks 
16 days ago by nhaliday
Joint Embedding of Query and Ad by Leveraging Implicit Feedback - Semantic Scholar
Sponsored search is at the center of a multibillion dollar market established by search technology. Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities. Lexical features derived from the text of both the query and ads play a significant role, complementing features based on historical click information. The purpose of this paper is to explore the use of word embedding techniques to generate effective text features that can capture not only lexical similarity between query and ads but also the latent user intents. We identify several potential weaknesses of the plain application of conventional word embedding methodologies for ad click prediction. These observations motivated us to propose a set of novel joint word embedding methods by leveraging implicit click feedback. We verify the effectiveness of these new word embedding models by adding features derived from the new models to the click prediction system of a commercial search engine. Our evaluation results clearly demonstrate the effectiveness of the proposed methods. To the best of our knowledge this work is the first successful application of word embedding techniques for the task of click prediction in sponsored search.
ctr  prediction  word  embeddings  papers  sponsored  search 
17 days ago by foodbaby

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