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

소개글

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.

참고 자료

“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