비즈니스와 데이터사이언스

논문 connected 된(논문의 참조들을 시각화해서 잘 보여준다.)https://www.connectedpapers.com/GPTstoreSciSpace와 Scholar GPTscispace는 gptstore에도 있고 https://typeset.i

Virtually every aspect of business is now open to data collection and often even instrumented for data collectionThis broad availability of data has l

1. From Business Problems to Data Mining Tasks 1.1. Classification and class probability estimation(분류와 계층확률 추정) Classification and class probability

0. Intro Predictive modeling as supervised segmentation 👉 A key to supervised data mining is that we have some target quantity we would like to pred

Predictive ModelingPredictive modeling involves finding a model of the target variable in terms of other descriptive attributesFrom the data we produc

0. Intro We are interested in patterns that generalize (일반화 시킬 수 있는 모델에 관심) – that predict well for instances we have not yet observed Finding chance

Business tasks involve reasoning from similar examples. If two things (people, companies, products) are similar in some ways they often share other ch

데이터 마이닝의 목적이 무엇긴가? 최적의 평가지표는 무엇인가? -> Nevertheless, there are various common issues and themes in evaluation, and frameworks and techniques for dealin

1. Ranking Instead of Classifying 일반적으로 분류(Classification) 모델은 특정 임계치(threshold)를 기준으로 예측값을 양분합니다. 그러나 실제 비즈니스 상황에서는 "어떤 사례는 거의 확실히 긍정 클래스이고, 어떤 사례는

1. Example: Targeting Online Consumers with Advertisements 2. Combining Evidence Probabilistically 2.2 Bayes' Rule 3. Applying Bayes’ Rule to Data