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Ensemble Modeling and Breast Cancer Prediction: A Wisdom of Crowds Approach to Machine Learning in R

Machine learning is a powerful tool for prediction, but with great power comes great responsibility. Data preparation/exploration and model selection is necessary, but does not have to be painful. This presentation will focus on machine learning as a process, from data preparation to decision making. Exploration will include a high level discussion of several machine learning techniques, using breast cancer data from the University of Wisconsin. Accuracy and output of models will be explained and combined into a custom ensemble method, considering the implications of Type I and Type II classification errors. Although the modeling is executed in R-Studio, the process is relevant to users of various backgrounds and tools.

Presented by

Lead Data Scientist

Nick is the Lead Data Scientist for Cardinal Solutions in Charlotte, NC. He holds a Master’s degree in Advanced Analytics from NC State University, as well as several SAS certifications. With a focus on predictive modeling, he is heavily involved in helping business partners implement data-driven strategy and measuring financial impact. Connect via LinkedIn.


June 29, 2017
1 - 2 p.m. EST

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