Branch-AI/one_exercise_per_file/week03/day03/ex05/readme.md

Exercise 5 Multi classification example

The goal of this exercise is to learn to use a neural network to classify a multiclass data set. The data set used is the Iris data set which allows to classify flower given basic features as flower's measurement.

Preliminary: - Split train test. Keep 20% for the test set. Use random_state=1. - Scale the data using Standard Scaler

1. Use the LabelBinarizer from Sckit-learn to create a one hot encoding of the target. As you know, the output layer of a multi-classification neural network shape is equal to the number of classes. The output layer expects to have a target with the same shape as its output layer.

2. Train a neural network on the train set and predict on the test set. The neural network should have 1 hidden layers. The expected accuracy on the test set is minimum 90%. Hint: inscrease the number of epochs Warning: Do no forget to evaluate the neural network on the SCALED test set.