Machine Learning on a Cancer Dataset - Part 19

In this video we start looking at neural networks in scikit-learn.

We begin with a few introductory concepts of how they work and what types there can be: simple neural networks and deep neural networks (with multiple hidden layers) - this is one way of classifying them.

Then we implement a multi-layer perceptron in scikit-learn. This is used via the MLPClassifier class and we train this classifier on our cancer data. We observe that it performs worse compared to the algorithms we've used so far. And in the next tutorial we'll see how we can improve its performance.

---

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:

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
6. Machine Learning on a Cancer Dataset - Part 16
7. Machine Learning on a Cancer Dataset - Part 17
8. Machine Learning on a Cancer Dataset - Part 18

---

---

To stay in touch with me, follow @cristi

---

Cristi Vlad, Self-Experimenter and Author