BSR 6806: Lecture 6 – Part 3

Cross Validation and scikit-learn

Daniel Clarke – ISMMS – Spring 2023

During Lecture 6 of BSR 6806, Daniel Clarke from ISMMS will be covering the topic of cross validation and scikit-learn. This lecture will focus on the use of cross validation techniques to evaluate the performance of machine learning models, as well as an introduction to the scikit-learn library for building and training machine learning models.

For those who are unfamiliar, cross validation is a technique used to assess how the results of a model will generalize to an independent dataset. This is achieved by splitting the dataset into multiple subsets, and then training the model on different combinations of these subsets. By doing so, we can obtain a more accurate estimate of the model’s performance.

Scikit-learn, on the other hand, is a popular open-source machine learning library for Python. It provides a range of tools for building and training machine learning models, as well as for validating and evaluating these models. With its easy-to-use interface and comprehensive documentation, scikit-learn is a go-to choice for many data scientists and machine learning practitioners.

During this lecture, Daniel Clarke will walk through the process of implementing cross validation using scikit-learn, and will demonstrate how to use the library’s functionality to evaluate the performance of different machine learning models. The lecture will also cover best practices for using cross validation and scikit-learn in real-world machine learning projects.

Overall, BSR 6806: Lecture 6 – Part 3 promises to be an insightful and practical session for anyone interested in harnessing the power of machine learning for their own data analysis and prediction tasks. Whether you’re a seasoned data scientist or just getting started with machine learning, this lecture is sure to offer valuable insights and practical tips that you can apply to your own projects.