Machine Learning on a Cancer Dataset - Part 16

In this machine learning video, we're gonna start exploring Random Forests in scikit-learn.

Random Forests are algorithms that can be used for both classification and regression tasks. We're gonna used them for classification. Most simply put, random forests are collections of decision trees (which we've explored in previous videos) with the caveat that they may be more advantageous due to the randomization that is applied for each tree comprising the forest.

In this specific video, I discus a few introductory concepts of random forests and then implement a classifier to our cancer dataset. For the detailed walkthrough, please see the video below.

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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.

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Previous videos in this series:

1. Machine Learning on a Cancer Dataset - Part 11
2. Machine Learning on a Cancer Dataset - Part 12
3. Machine Learning on a Cancer Dataset - Part 13
4. Machine Learning on a Cancer Dataset - Part 14
5. Machine Learning on a Cancer Dataset - Part 15

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To stay in touch with me, follow @cristi

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Cristi Vlad, Self-Experimenter and Author