Confidence of Gaussian Processes

Masinõpe on arvutiteaduse valdkond, mis tegeleb arvutisüsteemide oskusega iseseisvalt õppida. Masinõppemeetodeid kasutatakse nii andmete kirjeldamiseks kui ka tunnustele väärtuste ennustamiseks. Kui masinõppemudelit kasutatakse reaalarvulise väärtuse ennustamiseks, siis nimetatakse seda regressiooniks. Praktikas on reaalarvulist väärtust ennustades tihti tarvis arvestada, et vääral ennustusel võivad olla kallid tagajärjed. Väärade ennustuste kahju aitab vähendada see, kui mudel oskab ise hinnata, kui täpne tema ennustus on. Üheks näiteks sellisest hinnangust on tagastada vahemik, kuhu mudel 95% tõenäosusega hindab olevat õige väärtuse. Selline lähenemine on Gaussi protsessidel põhineva regressioonimudeli eriliseks omaduseks ning seda vahemikku nimetatakse usaldusvahemikuks. On oluline, et mudeli hinnang enda täpsuse kohta vastaks tegelikkusele ning et mudel ei hindaks end liiga enesekindlalt. Masinõppemudelite usaldusväärsuse hindamine on oluline, sest selliste mudelitega tarkvara kätte on tänapäeval usaldatud üha vastutusrikkamate otsuste langetamine. Antud bakalaureusetöö keskendub Gaussi protsessidel põhineva regressioonimudeli enesekindluse uurimisele. Antud töös uuritakse, kui tihti satuvad ennustatavate väärtuste tegelikud väärtused vahemikku, kuhu mudel hindab nende sattumise 95% tõenäosusega. Mõõtmised 6651 mudelil näitavad, et suurem osa päris märgendeid satuvad usaldusvahemikku oluliselt harvem kui 95% juhtudest ehk et Gaussi protsesside mudel on liigselt enesekindel. Keskmiseks usaldusvahemikku kuulunud osakaaluks on 0,93. Töö peamine tulemus on, et 73% mõõtetulemustest on madalamad kui võiks olla eelnevalt nimetatud tõenäosuse järgi. Ühtlasi on märkimisväärne see, et kõige väiksemate ja kõige suuremate väärtustega sisendväärtuste puhul on mudel rohkem liigselt enesekindel. Gaussi protsesside usaldusvahemiku uurimise näol on tegemist millegagi, midaei ole varem uuritud. Tänu käesolevale tööle on olemas hinnang Gaussi protsesside regressioonimudeli usaldusväärsusele ning selle töö tulemus aitab Gaussi protsesside kasutajatel võtta arvesse antud meetodi liigset enesekindlust.Machine learning is a field in computer science that provides computer systems with the ability to learn independently. Machine learning methods are used for both descriptive and predictive purposes. When a machine learning model is used to predict a real valued number it is called regression. In practice, it is often important in regression to take into account that false predictions might have severe consequences. To avoid such false predictions, it is helpful if the model is able to rate how accurate its prediction is. An example of this is for the model to provide an interval where it predicts the true value with 95% certainty. This approach is unique to Gaussian process regression model and thisinterval is called confidence interval. It is important that the model rates itself accurately and not overly confidently. Evaluating confidence of machine learning models is important since software solutions equipped with machine learning algorithms are becoming more common and are being trusted with decisions that require more responsibility. This Bachelor’s thesis focuses on the confidence of Gaussian process regression models. This research examines how often are true values contained in the intervals where model predicts them with 95% probability. Measurement results on 6651 models show that the majority of true labels are included in the confidence interval in less than 95% of cases, which means that Gaussian process regression model is overconfident. Mean ratio of true labels in confidence intervals per model was 0.93. Main result of the research is that for 73% of the models the confidence intervalcontained less true labels than was expected by the probability. It is noteworthy that for input values that had smallest or largest values the model was more often overconfident.Confidence of Gaussian processes has not been researched before and this research provides evaluation on how reliable are Gaussian processes. The results of this thesis enable users of Gaussian processes models to consider overconfidence of models

How should the completeness and quality of curated nanomaterial data be evaluated?

Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials' behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough exposure risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models”, with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles

Dekabrist Wilhelm Küchelbecker

https://www.ester.ee/record=b5637248*es

Defitsiit kui osa nõukogudeaegsest argielust Eesti NSV-s : fenomenoloogiline vaatepunkt

Teadusmagistritöö elektrooniline versioon ei sisalda publikatsioone

Comparison of category-level, item-level and general sales forecasting models

Sales forecasting is the process of estimating future sales. In this thesis, multiple methods are tested out for achieving best forecasting accuracy with lowest computational requirements. Three families of methods are investigated: a traditional statistical forecasting approach (ARIMA), classical machine learning techniques (specifically ensemble methods) and a third one based on deep learning methods (specifically recurrent neural networks with LSTM architectures). The study uses real-world sales transaction data from a large retail company in a Baltic country and the aim of this thesis is to improve their current sales forecasting system. Here we show that improving on their current sales forecasting is possible and additionally analyse the influence of promotional sales to prediction accuracy. The results show that using a combination of multiple item-level decision tree-based ensemble models yields the best prediction accuracy with regard to training complexity. Additionally, when comparing accuracy of forecasts for promotional sales and non-promotional sales, a variant of ARIMA achieves the most accurate results when forecasting promotional sales

"The Needs of Consumers Oblige": Daily Problems and Criticism of the System in Public Letters in the 1960s and 1980s

<p>This article examines public letters as an expression of everyday household problems and practices. The sources used are those letters from among the archival materials of the Tartu Retail Trade Association and the Tallinn Markets Administration that have survived from the 1960s and 1980s. Using qualitative thematic analysis, I examine the points of criticism that have arisen in connection with retail trade and the (deficit) reality behind it, along with specific behavioural practices. In addition, my aim is to show the relations between individuals and the state, and their dynamics, by presenting criticism with the help of discourse analysis.</p

QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

Abstract Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. </jats:sec