Machine Learning on a Cancer Dataset - Part 13
One of the best ways we learn is through visual representation. This is very helpful especially when we're dealing with 'sciency' stuff that involves math and algorithms.
Recall from the last video and blogpost (part 12) that we've trained a Decision Tree classifier in scikit-learn. To better understand how it decides on whether or not a tumor is malignant or benign, we're gonna do some visualization in this video.
I'm using the graphviz library in Python for this purpose. For the detailed walk-through, please see the video below.
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
- Machine Learning on a Cancer Dataset - Part 10
- Machine Learning on a Cancer Dataset - Part 11
- Machine Learning on a Cancer Dataset - Part 12
To stay in touch with me, follow @cristi
#machine-learning #science #python
Cristi Vlad, Self-Experimenter and Author