Machine Learning on a Cancer Dataset - Part 10
This is the third and last video on Logistic Regression on the cancer dataset in scikit-learn; and the 10th video in the machine learning series.
This is all about visualization; in it I discuss how modifying the 'C' parameter in Logistic Regression, which controls the strength of regularization, impacts the results and the performance of the algorithm. We can visualize, using matplotlib, how a lower value of 'C' meaning stronger regularization leads to a tendency to shift the coefficients toward zero, but not reaching 0.
I also discuss the decision making (or the prediction) behind Logistic Regression and other linear models in scikit-learn. They basically depend on the simple equation of a line (y=mx+n); remember from math class?
See the video below for a walk-through if you have no freakin' clue of what I'm talking about here.
As a reminder:
In this series I'm going to explore the cancer dataset that comes pre-loaded with scikit-learn. The purpose is to train the classifiers on this dataset, which consists of labeled data: ~569 tumor samples, each labeled malignant or benign, and then use them on new, unlabeled data.
Previous videos in this series:
- Machine Learning on a Cancer Dataset - Part 1
- Machine Learning on a Cancer Dataset - Part 2
- Machine Learning on a Cancer Dataset - Part 3
- Machine Learning on a Cancer Dataset - Part 4
- Machine Learning on a Cancer Dataset - Part 5
- Machine Learning on a Cancer Dataset - Part 6
- Machine Learning on a Cancer Dataset - Part 7
- Machine Learning on a Cancer Dataset - Part 8
- Machine Learning on a Cancer Dataset - Part 9
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Cristi Vlad, Self-Experimenter and Author