Classify structured (tabular) data with a neural network.
Description
This example uses a neural network to classify tabular data representing different flowers. The data used for each flower are the petal length and width as well as the sepal length and width. The goal is to predict what kind of flower it is based on those features of each data point. The data comes from the famous Iris flower data set.
Instructions
Change the hyperparameters as you would like them to be.
Add the number of neurons for the the number of layers you want to have in the required neural network.
Train the model.
A Model Summery Tab will appear you can maximise it or hide it.
You can visualize the architecture by clicking on the NN Structure button.
If you want to visualize the coloured edges(coloured according to their weight sign),you can click on the
checkbox and click on NN Structure again, the edges will appear coloured and varied in width and color
intensity on the basis of the weight magnitude.
You can edit the properties in first row of "Test Examples" to generate a prediction for those data points.
Data Visualization
Controls
Status
Training Progress
Visualization of Neural Network
Test Examples
| Petal length | Petal width | Sepal length | Sepal width | True class | Predicted class | Class Probabilities |
|---|---|---|---|---|---|---|