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.
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:
Preliminary:
- [Load the dataset from `sklearn`.](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)
- Split train test. Keep 20% for the test set. Use `random_state=1`.
- Split train test. Keep 20% for the test set. Use `random_state=1`.
- Scale the data using Standard Scaler
- Scale the data using Standard Scaler
@ -11,4 +12,4 @@ Preliminary:
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%.
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
*Hint*: inscrease the number of epochs
**Warning**: Do no forget to evaluate the neural network on the **SCALED** test set.
**Warning**: Do no forget to evaluate the neural network on the **SCALED** test set.
brad-gh
2 years ago
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