[A+ 연세대][2019 1학기 진행] 미생물및생물공학기초실험 Lab 10: Quantitative Structure Activity Relationship 결과레포트

소개

2019년 1학기에 진행된 생명공학과 미생물및생물공학기초실험 과목에서 최종 성적 A+를 받은 결과레포트입니다. 결과레포트가 YSCEC을 이용하여 Turnitin 검수과정을 거치지 않고 메일과 하드카피 제출로 이루어지기 때문에 해당 레포트 내용을 조금씩만 변경하여 제출하시면 수월하게 A+를 받아가실 수 있을 것입니다. 글꼴은 맑은 고딕(본문) 혹은 a타이틀고딕(네이버에서 다운로드 가능)으로 작성하였습니다.

목차

1. Quiz
2. Reference

본문내용

Overfitting simply said is a model that models the training data too well. Overfitting happens in the case where a model learns the deal and noise in the training data to an extent that it gives negative impacts to the performance to the models which are on a new data set. Through the process of machine learning, the noise or random fluctuations in the training data has been picked up and learned as concepts by the model.

참고자료

· “The Application of Machine Learning in Biology”, Ragothaman Yennamalli, Kolabtree Blog, Web, May 29th 2019 <https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/>
· “Machine Learning and Its Application to Biology”, Adi L Tarca, Vincent J Carey, Xue-wen Chen, Roberto Romero, Sorin Draghici, PLoS Comput Biol 3(6): e116, June 2007
· “Overfitting in Machine Learning: What is it and how to prevent it”, EliteDataScience, Web, May 29th 2019 <https://elitedatascience.com/overfitting-in-machine-learning>
· “Overfitting and Underfitting With Machine Learning Algorithms”, Jason Brownlee, MachineLearningMastery, Web, May 29th 2019 <https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/>
· “Machine learning and complex biological data”, Chunming Xu, Scott A. Jackson, Genome Biology, April 2019