Feed Forward Single/Multiple-Hidden Layer Classifier for Thyroid Dataset

Description

Python (sklearn-based) implementation that explores how different parameters impact a feed-forward neural network with single/multiple hidden layers.

A brief analysis of the results is provided in Portuguese. It was submitted as an assignment of a graduate course named Connectionist Artificial Intelligence at UFSC, Brazil.

In short, two normalization methods are evaluated (minmax and Yeo-Johnson) in a thyroid dataset from UCI ported to matlab with multiple training algorithms, hidden layers, learning rate (alpha), epochs and activation functions.

Normalization

Before normalization	MinMax normalization	Yeo-Johnson normalization

Results