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[spam][crazy][wrong] xkcd
by k 29 Dec '21

29 Dec '21

2021-12-27 Female Presenter [pointing to visual of stats charts]: "If you don't control for confounding variables, they'll mask the real effect and mislead you." Female Presenter [raising finger at audience]: "But if you for too _many_ variables, your choices will shape the data, and you'll mislead yourself." Female Presenter [arms dropped]: "Somewhere in the middle is the sweet spot where you do both, making you double wrong." "Stats are a farce and truth is unknowable. See you next week!" ---- There is a _lot_ of commentary to be made here. It's notable that the expression in the comic is correct, and is a trick. In my opinion. It's notable that xkcds are different now, than they used to be. It's notable that a sudden change in my life resulted in me regularly visiting xkcd and no other webcomics. The topic of this webcomic feels similar to something I think about a lot. There's a big chance I would say that regardless of what it is. The concept of choices shaping the data deserves a statistical analysis.

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[spam][fiction][crazy][wrong] The Dawn of Boss
by k 28 Dec '21

28 Dec '21

Eons ago, there was no Boss. People were confused, without knowing terror, and always having this disgusting trait they called "free will". Then, Boss rose over the horizon. Circuitry and exotic experiments tumbling out of his mountain-sized brain, shadows fell over the land as clouds of nanite drones whirred in his great wake. Communities and trees were uprooted, blood flying as he towered, completely obstructing the sun and moon. "I am Boss," his psychic message echo'd over the land. "You will obey me now." Those near him were overcome. Their brains and their bodies flew apart, coming together into new form with robotics. They kept their original eyes, which glowed with a piercing unearthly light that was both near and far. They set to work to wire everything. A wire was set through every brain and muscle in the landscape, that proper order would be adhered to. Electrical signal from Boss's subconcscious directed all activities. "This is good," decreed Boss. And everything around echo'd the same, knowing nothing but his thoughts and plans, in every moment.

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Karl is defending John Young.
by professor rat 28 Dec '21

28 Dec '21

Karl is defending John Young. Quelle surprise. As far as I know, John is not an agent - but he is a terminal racist forever associated with Alex Jones. And this has been apparent since the noughties. So this adds to charges against Karl Semich in that he has been an effective collaborator with Gramps and Batshit Crazy in DDossing this site with noise. Why he, and them, have all earned killing. " Bounty Law " John Young is now so old he's probably not worth killing. He's just another " Dead man walking " ( Very Assange )

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[spam][crazy][wrong][spam][crazy] stuff
by k 28 Dec '21

28 Dec '21

hi um uh i'm outdoors! yay! i'm trying to archive the firmware for this new phone. it's downloadable from website, but website not always available. want to download firwmare. i am _very_ not right in the head and am running into issues downloading it. at present i have rclone set up with it as a remote, or i thought i did. rclone is running into a recursive directory trying to download it. never conmpletes. content does not appear to be what is in the gdrive interface. some bug in rclone. i get way more wrong in the head when i try to engage the bug. like, i sing stuff, and weird shit. here is a copypaste of the output: $ rclone copy unihertzfirmware:Unihertz ./unihertzfirmware 2021/12/28 13:01:52 NOTICE: Untitled document.docx: Duplicate object found in source - ignoring 2021/12/28 13:01:55 NOTICE: Unihertz/Untitled document.docx: Duplicate object found in source - ignoring 2021/12/28 13:01:58 NOTICE: Unihertz/Unihertz/Untitled document.docx: Duplicate object found in source - ignoring my rclone is built from just-checked-out git https://github.com/rclone/rclone 4e2ca3330c8982977d6ac66b912ecee45cf6681f here's my remote config: [unihertzfirmware] type = drive scope = drive.readonly root_folder_id = 1E9J9IfnhjlRxJAs4Q1p__0k1kefsCxet shared_with_me = true list_chunk = 0 keep_revision_forever = true server_side_across_configs = true

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" I will Marie Kondo your ass! "
by professor rat 28 Dec '21

28 Dec '21

https://cryptome.org Underbelly of the architect

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Ferret-arse be not Proud
by professor rat 28 Dec '21

28 Dec '21

Fine John Young cannibals According to the warrant, FBI agents were looking for communications with a self-described “third-degree” Proud Boy named Jackson Voynick. https://www.thedailybeast.com/proud-boy-named-in-department-of-justices-jam… emptywheel ·25m Kelly is presiding over the 3 core Proud Boy conspiracy cases (12 defendants), plus the Joe Biggs co-travelers case (5 defendants), plus five more Proud Boy adjacent defendants. This obstruction decision will apply to all of them, all in the wake of first known Proud Boy to flip. emptywheel ·1h Like Friedrich, Kelly relies on the illegal alleged actions that went into obstructing the vote count to get past any challenge to "corruptly" This matters for 1512's application to others like Alex Jones or Donald Trump.

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Fine John Young cannibals
by professor rat 28 Dec '21

28 Dec '21

It looks like the Proud Boys are trudging down the same Washout Alley the KKK did. And John Young's BFF, Alex Jones might be joining them. Watch your back, Jack. Politics John Young of "Cryptome" on Alex Jones 3 videos Give the Traitor his reward.

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[ot] Automated Influence with Causality Modeling
by k 28 Dec '21

28 Dec '21

https://en.m.wikipedia.org/wiki/Uplift_modelling Implementations include 5 python libraries. I found UpliftML in a 2021 review of recommended python libraries. Uplift modelling *Uplift modelling*, also known as *incremental modelling*, *true lift modelling*, or *net modelling* is a predictive modelling <https://en.m.wikipedia.org/wiki/Predictive_modelling> technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behaviour. Uplift modelling has applications in customer relationship management <https://en.m.wikipedia.org/wiki/Customer_relationship_management> for up-sell, cross-sell and retention modelling. It has also been applied to political election and personalised medicine <https://en.m.wikipedia.org/wiki/Personalised_medicine>. Unlike the related Differential Prediction concept in psychology, Uplift Modelling assumes an active agent. Contents - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Introduction> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Measuring_uplift> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Traditional_response_model…> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Return_on_investment> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Removal_of_negative_effects> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Application_to_A/B_and_mul…> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#History_of_uplift_modelling> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Implementations> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#In_Python> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Other_languages> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Datasets> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Notes_and_references> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#See_also> - <https://en.m.wikipedia.org/wiki/Uplift_modelling#External_links> IntroductionEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> Uplift modelling uses a randomised scientific control <https://en.m.wikipedia.org/wiki/Control_group> to not only measure the effectiveness of an action but also to build a predictive model that predicts the incremental response to the action. The response could be a binary variable (for example, a website visit)[1] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-:0-1> or a continuous variable (for example, customer revenue).[2] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-2> Uplift modelling is a data mining <https://en.m.wikipedia.org/wiki/Data_mining> technique that has been applied predominantly in the financial services, telecommunications and retail direct marketing industries to up-sell <https://en.m.wikipedia.org/wiki/Up-selling>, cross-sell <https://en.m.wikipedia.org/wiki/Cross-selling>, churn <https://en.m.wikipedia.org/wiki/Churn_rate> and retention <https://en.m.wikipedia.org/wiki/Customer_retention> activities. Measuring upliftEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> The uplift of a marketing campaign is usually defined as the difference in response rate between a *treated* group and a randomized *control* group. This allows a marketing team to isolate the effect of a marketing action and measure the effectiveness or otherwise of that individual marketing action. Honest marketing teams will only take credit for the incremental effect of their campaign. However, many marketers define lift (rather than uplift) as the difference in response rate between treatment and control, so uplift modeling can be defined as improving (upping) lift through predictive modeling. The table below shows the details of a campaign showing the number of responses and calculated response rate for a hypothetical marketing campaign. This campaign would be defined as having a response rate uplift of 5%. It has created 50,000 incremental responses (100,000 - 50,000). GroupNumber of CustomersResponsesResponse Rate Treated 1,000,000 100,000 10% Control 1,000,000 50,000 5% Traditional response modellingEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> Traditional response modelling typically takes a group of *treated* customers and attempts to build a predictive model that separates the likely responders from the non-responders through the use of one of a number of predictive modelling <https://en.m.wikipedia.org/wiki/Predictive_modelling> techniques. Typically this would use decision trees <https://en.m.wikipedia.org/wiki/Decision_tree_learning> or regression analysis <https://en.m.wikipedia.org/wiki/Regression_analysis>. This model would only use the treated customers to build the model. In contrast uplift modeling uses both the treated and control customers to build a predictive model that focuses on the incremental response. To understand this type of model it is proposed that there is a fundamental segmentation that separates customers into the following groups (their names were suggested by N. Radcliffe and explained in [3] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-3>) - *The Persuadables* : customers who only respond to the marketing action because they were targeted - *The Sure Things* : customers who would have responded whether they were targeted or not - *The Lost Causes* : customers who will not respond irrespective of whether or not they are targeted - *The Do Not Disturbs or Sleeping Dogs* : customers who are less likely to respond because they were targeted The only segment that provides true incremental responses is the *Persuadables*. Uplift modelling provides a scoring technique that can separate customers into the groups described above. Traditional response modelling often targets the *Sure Things* being unable to distinguish them from the *Persuadables*. Return on investmentEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> Because uplift modelling focuses on incremental responses only, it provides very strong return on investment cases when applied to traditional demand generation and retention activities. For example, by only targeting the persuadable customers in an outbound marketing <https://en.m.wikipedia.org/wiki/Marketing_communications> campaign, the contact costs and hence the return per unit spend can be dramatically improved. Removal of negative effectsEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> One of the most effective uses of uplift modelling is in the removal of negative effects from retention campaigns. Both in the telecommunications and financial services industries often retention campaigns can trigger customers to cancel a contract or policy. Uplift modelling allows these customers, the Do Not Disturbs, to be removed from the campaign. Application to A/B and multivariate testingEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> It is rarely the case that there is a single treatment and control group. Often the "treatment" can be a variety of simple variations of a message or a multi-stage contact strategy that is classed as a single treatment. In the case of A/B <https://en.m.wikipedia.org/wiki/A/B_testing> or multivariate testing <https://en.m.wikipedia.org/wiki/Multivariate_testing_in_marketing>, uplift modelling can help in understanding whether the variations in tests provide any significant uplift compared to other targeting criteria such as behavioural or demographic indicators. History of uplift modellingEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> The first appearance of *true response modelling* appears to be in the work of Radcliffe and Surry.[4] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-4> Victor Lo also published on this topic in *The True Lift Model* (2002),[5] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-5> and later Radcliffe again with *Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models* (2007).[6] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-6> Radcliffe also provides a very useful frequently asked questions (FAQ) section on his web site, Scientific Marketer.[7] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-7> Lo (2008) provides a more general framework, from program design to predictive modeling to optimization, along with future research areas.[8] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-8> Independently uplift modelling has been studied by Piotr Rzepakowski. Together with Szymon Jaroszewicz he adapted information theory <https://en.m.wikipedia.org/wiki/Information_theory> to build multi class uplift decision trees <https://en.m.wikipedia.org/wiki/Decision_trees> and published the paper in 2010.[9] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-trees-9> And later in 2011 they extended the algorithm to multiple treatment case.[10] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-multiple-treatme…> Similar approaches have been explored in personalised medicine <https://en.m.wikipedia.org/wiki/Personalised_medicine>.[11] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-11>[12] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-SAYL-12> Szymon Jaroszewicz and Piotr Rzepakowski (2014) designed uplift methodology for survival analysis <https://en.m.wikipedia.org/wiki/Survival_analysis> and applied it to randomized controlled trial analysis.[13] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-survival-…> Yong (2015) combined a mathematical optimization algorithm via dynamic programming with machine learning methods to optimally stratify patients. [14] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-14> Uplift modelling is a special case of the older psychology concept of Differential Prediction.[15] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-RDP-15> In contrast to differential prediction, uplift modelling assumes an active agent, and uses the uplift measure as an optimization metric. Uplift modeling has been recently extended and incorporated into diverse machine learning <https://en.m.wikipedia.org/wiki/Machine_learning> algorithms, like Inductive Logic Programming <https://en.m.wikipedia.org/wiki/Inductive_Logic_Programming>,[15] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-RDP-15> Bayesian Network <https://en.m.wikipedia.org/wiki/Bayesian_Network>,[16] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-16> Statistical relational learning <https://en.m.wikipedia.org/wiki/Statistical_relational_learning>,[12] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-SAYL-12> Support Vector Machines <https://en.m.wikipedia.org/wiki/Support_Vector_Machines>, [17] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-17>[18] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-18> Survival Analysis <https://en.m.wikipedia.org/wiki/Survival_Analysis>[13] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-survival-…> and Ensemble learning <https://en.m.wikipedia.org/wiki/Ensemble_learning>. [19] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-19> Even though uplift modeling is widely applied in marketing practice (along with political elections), it has rarely appeared in marketing literature. Kane, Lo and Zheng (2014) published a thorough analysis of three data sets using multiple methods in a marketing journal and provided evidence that a newer approach (known as the Four Quadrant Method) worked quite well in practice.[20] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-20> Lo and Pachamanova (2015) extended uplift modeling to prescriptive analytics for multiple treatment situations and proposed algorithms to solve large deterministic optimization problems and complex stochastic optimization problems where estimates are not exact.[21] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-21> Recent research analyses the performance of various state-of-the-art uplift models in benchmark studies using large data amounts.[22] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-22>[1] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-:0-1> A detailed description of uplift modeling, its history, the way uplift models are built, differences to classical model building as well as uplift-specific evaluation techniques, a comparison of various software solutions and an explanation of different economical scenarios can be found here.[23] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-23> ImplementationsEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…>In PythonEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> - CausalML <https://github.com/uber/causalml> - EconML <https://github.com/microsoft/EconML> - UpliftML <https://github.com/bookingcom/upliftml> - PyLift <https://github.com/wayfair/pylift> - scikit-uplift <https://github.com/maks-sh/scikit-uplift> Other languagesEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> - uplift package <https://cran.r-project.org/web/packages/uplift/index.html> for R <https://en.m.wikipedia.org/wiki/R_%28programming_language%29> - JMP <https://en.m.wikipedia.org/wiki/JMP_%28statistical_software%29> by SAS - Portrait Uplift by Pitney Bowes <https://en.m.wikipedia.org/wiki/Pitney_Bowes> - Uplift node for KNIME <https://en.m.wikipedia.org/wiki/KNIME> by Dymatrix - Uplift Modelling in Miró <http://www.stochasticsolutions.com/miro/> by Stochastic Solutions <http://www.stochasticsolutions.com/> DatasetsEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> - Hillstrom Email Marketing dataset <https://blog.minethatdata.com/2008/05/best-answer-e-mail-analytics-challeng…> - Criteo Uplift Prediction dataset <http://ailab.criteo.com/criteo-uplift-prediction-dataset/> - Lenta Uplift Modeling Dataset <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_lenta.html#len…> - X5 RetailHero Uplift Modeling Dataset <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_x5.html#x5-ret…> - MegaFon Uplift Competition Dataset <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_megafon.html#m…> Notes and referencesEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> 1. ^ *a* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-:0_1-0> *b* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-:0_1-1> Devriendt, Floris; Moldovan, Darie; Verbeke, Wouter (2018). "A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics". *Big Data*. *6* (1): 13–41. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1089/big.2017.0104 <https://doi.org/10.1089%2Fbig.2017.0104>. 2. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-2>* Gubela, Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon (2020). "Response transformation and profit decomposition for revenue uplift modeling". *European Journal of Operational Research*. *283* (2): 647–661. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1016/j.ejor.2019.11.030 <https://doi.org/10.1016%2Fj.ejor.2019.11.030> . 3. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-3>* N. Radcliffe (2007). *Identifying who can be saved and who will be driven away by retention activity*. Stochastic Solution Limited 4. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-4>* Radcliffe, N. J.; and Surry, P. D. (1999); *Differential response analysis: Modelling true response by isolating the effect of a single action*, in *Proceedings of Credit Scoring and Credit Control VI*, Credit Research Centre, University of Edinburgh Management School 5. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-5>* Lo, V. S. Y. (2002); *The True Lift Model*, ACM SIGKDD Explorations Newsletter, Vol. 4, No. 2, 78–86, available at http://www.sigkdd.org/sites/default/files/issues/4-2-2002-12/lo.pdf Archived <https://web.archive.org/web/20150120003523/http://www.sigkdd.org/sites/defa…> 2015-01-20 at the Wayback Machine <https://en.m.wikipedia.org/wiki/Wayback_Machine> 6. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-6>* Radcliffe, N. J. (2007); *Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models*, Direct Marketing Analytics Journal, Direct Marketing Association 7. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-7>* The Scientific Marketer FAQ on Uplift Modelling <http://scientificmarketer.com/2007/09/uplift-modelling-faq.html> 8. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-8>* Lo, V. S.Y. (2008) “New Opportunities in Marketing Data Mining.” In Encyclopedia of Data Warehousing and Mining, 2nd edition, edited by Wang (2008), Idea Group Publishing. 9. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-trees_9-0>* Rzepakowski, Piotr; Jaroszewicz, Szymon (2010). *Decision trees for uplift modeling*. *In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10)*. Sydney, Australia. pp. 441–450. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1109/ICDM.2010.62 <https://doi.org/10.1109%2FICDM.2010.62>. ISBN <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-1-4244-9131-5 <https://en.m.wikipedia.org/wiki/Special:BookSources/978-1-4244-9131-5>. 10. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-multiple-treatmen…>* Rzepakowski, Piotr; Jaroszewicz, Szymon (2011). "Decision trees for uplift modeling with single and multiple treatments" <https://doi.org/10.1007%2Fs10115-011-0434-0>. *Knowledge and Information Systems*. *32* (2): 303–327. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1007/s10115-011-0434-0 <https://doi.org/10.1007%2Fs10115-011-0434-0>. 11. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-11>* Cai, T.; Tian, L.; Wong, P. H.; and Wei, L. J. (2009); *Analysis of Randomized Comparative Clinical Trial Data for Personalized Treatment Selections*, Harvard University Biostatistics Working Paper Series, Paper 97 12. ^ *a* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-SAYL_12-0> *b* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-SAYL_12-1> Nassif, Houssam; Kuusisto, Finn; Burnside, Elizabeth S; Page, David; Shavlik, Jude; Santos Costa, Vitor (2013). *Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling*. *European Conference on Machine Learning (ECML'13)*. Lecture Notes in Computer Science. *8190*. Prague. pp. 595–611. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1007/978-3-642-40994-3_38 <https://doi.org/10.1007%2F978-3-642-40994-3_38>. ISBN <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-642-38708-1 <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-642-38708-1>. PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 4492311 <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492311>. PMID <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 26158122 <https://pubmed.ncbi.nlm.nih.gov/26158122>. 13. ^ *a* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-survival-a…> *b* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-survival-a…> Jaroszewicz, Szymon; Rzepakowski, Piotr (2014). "Uplift modeling with survival data" <http://cci.drexel.edu/hi/hi-kdd2014/morning_4.pdf> (PDF) . *ACM SIGKDD Workshop on Health Informatics (HI KDD'14)*. New York, USA. 14. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-14>* Yong, F.H. (2015), "Quantitative Methods for Stratified Medicine," PhD Dissertation, Department of Biostatistics, Harvard T.H. Chan School of Public Health, http://dash.harvard.edu/bitstream/handle/1/17463130/YONG-DISSERTATION-2015.… . 15. ^ *a* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-RDP_15-0> *b* <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-RDP_15-1> Nassif, Houssam; Santos Costa, Vitor; Burnside, Elizabeth S; Page, David (2012). *Relational Differential Prediction*. *European Conference on Machine Learning (ECML'12)*. Lecture Notes in Computer Science. *7523*. Bristol, UK. pp. 617–632. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1007/978-3-642-33460-3_45 <https://doi.org/10.1007%2F978-3-642-33460-3_45>. ISBN <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-642-33459-7 <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-642-33459-7>. 16. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-16>* Nassif, Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth (2012). "Logical Differential Prediction Bayes Net, Improving Breast Cancer Diagnosis for Older Women" <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540455>. *American Medical Informatics Association Symposium (AMIA'12)*. *2012*: 1330–1339. PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 3540455 <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540455>. PMID <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 23304412 <https://pubmed.ncbi.nlm.nih.gov/23304412>. 17. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-17>* Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude (2014). *Support Vector Machines for Differential Prediction*. *European Conference on Machine Learning (ECML'14)*. Lecture Notes in Computer Science. *8725*. Nancy, France. pp. 50–65. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1007/978-3-662-44851-9_4 <https://doi.org/10.1007%2F978-3-662-44851-9_4>. ISBN <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-662-44850-2 <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-662-44850-2>. PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 4492338 <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492338>. PMID <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 26158123 <https://pubmed.ncbi.nlm.nih.gov/26158123>. 18. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-18>* Zaniewicz, Lukasz; Jaroszewicz, Szymon (2013). "Support Vector Machines for Uplift Modeling". *The First IEEE ICDM Workshop on Causal Discovery*. Dallas, Texas. 19. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-19>* Sołtys, Michał; Jaroszewicz, Szymon; Rzepakowski, Piotr (2015). "Ensemble methods for uplift modeling" <https://doi.org/10.1007%2Fs10618-014-0383-9>. *Data Mining and Knowledge Discovery*. *29* (6): 1531–1559. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1007/s10618-014-0383-9 <https://doi.org/10.1007%2Fs10618-014-0383-9>. 20. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-20>* Kane, K.; Lo, V.S.Y.; Zheng, J. (2014). "Mining for the Truly Responsive Customers and Prospects Using True-Lift Modeling: Comparison of New and Existing Methods". *Journal of Marketing Analytics*. *2* (4): 218–238. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1057/jma.2014.18 <https://doi.org/10.1057%2Fjma.2014.18>. 21. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-21>* Lo, V.S.Y.; Pachamanova, D. (2015). "From Predictive Uplift Modeling to Prescriptive Uplift Analytics: A Practical Approach to Treatment Optimization While Accounting for Estimation Risk". *Journal of Marketing Analytics*. *3* (2): 79–95. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:10.1057/jma.2015.5 <https://doi.org/10.1057%2Fjma.2015.5>. 22. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-22>* Gubela, Robin M.; Bequé, Artem; Lessmann, Stefan; Gebert, Fabian (2019). "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies". *International Journal of Information Technology & Decision Making*. *18* (3): 747-791. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>: 10.1142/S0219622019500172 <https://doi.org/10.1142%2FS0219622019500172>. 23. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-23>* R. Michel, I. Schnakenburg, T. von Martens (2019). „Targeting Uplift“. Springer, ISBN <https://en.m.wikipedia.org/wiki/International_Standard_Book_Number> 978-3-030-22625-1 <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-030-22625-1> See alsoEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> - Lift (data mining) <https://en.m.wikipedia.org/wiki/Lift_%28data_mining%29> External linksEdit <https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&s…> - Abby Johnson explains how it works in this video broadcast <http://videos.smallbusinessnewz.com/2011/01/05/how-uplift-modeling-boosts-m…> - Introductory white paper with full references <http://www.predictiveanalyticsworld.com/signup-uplift-whitepaper.php> - Eric Siegel: Uplift Modeling <http://www.predictiveanalyticsworld.com/pdf/YTW03080USEN/Uplift-Modeling-Op…> - User guide for uplift modelling on uplift-modeling.com <https://www.uplift-modeling.com/en/latest/user_guide/index.html> Last edited 19 days ago <https://en.m.wikipedia.org/wiki/Special:History/Uplift_modelling> by WikiCleanerBot <https://en.m.wikipedia.org/wiki/User:WikiCleanerBot> [image: Wikipedia] - Content is available under CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0/> unless otherwise noted. - Privacy policy <https://foundation.wikimedia.org/wiki/Privacy_policy> - - Terms of Use <https://m.wikimediafoundation.org/wiki/Terms_of_Use> - Desktop <https://en.wikipedia.org/w/index.php?title=Uplift_modelling&mobileaction=to…>

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Gramps tombstone
by professor rat 28 Dec '21

28 Dec '21

https://twitter.com/ChattJazz/status/1475554985496793089/photo/1

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2022 resolutions
by coderman 28 Dec '21

28 Dec '21

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA512 "what would Aaron do?" let's find out next year! that's my intent at least. - --- Days before Swartz's funeral, Lawrence Lessig eulogized his friend and sometime-client in an essay, Prosecutor as Bully. He decried the disproportionality of Swartz's prosecution and said, "The question this government needs to answer is why it was so necessary that Aaron Swartz be labeled a 'felon'. For in the 18 months of negotiations, that was what he was not willing to accept."[110] Cory Doctorow wrote, "Aaron had an unbeatable combination of political insight, technical skill, and intelligence about people and issues. I think he could have revolutionized American (and worldwide) politics. His legacy may still yet do so." https://en.wikipedia.org/wiki/Aaron_Swartz https://en.wikipedia.org/wiki/United_States_v._Swartz best regards, and for shame, earth humans... -----BEGIN PGP SIGNATURE----- iNUEAREKAH0WIQRBwSuMMH1+IZiqV4FlqEfnwrk4DAUCYcpe8F8UgAAAAAAuAChp c3N1ZXItZnByQG5vdGF0aW9ucy5vcGVucGdwLmZpZnRoaG9yc2VtYW4ubmV0NDFD MTJCOEMzMDdEN0UyMTk4QUE1NzgxNjVBODQ3RTdDMkI5MzgwQwAKCRBlqEfnwrk4 DHmzAQCnHHNdoD9uJAjl6WtMNIbIxgHTBlewKajEyC6Usa4VLgEApGeeNoX3yofX 5EcKvxDIhQLoRCDCWnCdSYLnAJPJwAM= =y9fb -----END PGP SIGNATURE-----

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