Machine Learning on a Cancer Dataset - Part 15
In this final video on Decision Tree, we recapitulate the advantages and disadvantages of this classifier for machine learning purposes. To be more specific, we've been using Decision Trees in scikit-learn to learn to classify tumors into benign or malignant.
We also look at the parameters that can be used/tuned to optimize this classifier so that it can lead to better decision making.
In the next iteration, we'll be looking into ensembles of trees as classifiers. These algorithms are often used as a better alternative to decision trees because they are less 'prone' to overfitting.
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 the series:
- Machine Learning on a Cancer Dataset - Part 11
- Machine Learning on a Cancer Dataset - Part 12
- Machine Learning on a Cancer Dataset - Part 13
- Machine Learning on a Cancer Dataset - Part 14
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Cristi Vlad, Self-Experimenter and Author