Classification and Regression with the BigML Dashboard

5.14 Takeaways

This chapter explains deepnets in detail. Here is a list of key points:

  • A deepnet is a supervised Machine Learning algorithm used to solve classification and regression problems.

  • A deepnet is built from a dataset available in BigML and used to make an evaluation, a single prediction, or a batch prediction. (See Figure 5.116 .)

  • You can create a deepnet with just one click or configure it as you wish. BigML provides several configuration options before creating your deepnet.

  • To create a deepnet you need a dataset containing at least one categorical or numeric field.

  • If you do not specify any Objective Field, BigML will take the last valid field in your dataset.

  • BigML allows you to include your numeric fields’ missing values as valid values to train your deepnet.

  • The PDP view provides a visual way to analyze a field impact on predictions given certain values for the rest of the fields.

  • You get all the objective field class probabilities along with the predicted class.

  • You need to evaluate your deepnet performance using data that the model has not seen before.

  • The ultimate goal in building a deepnet is being able to make predictions with it.

  • BigML allows you to quickly make predictions for single instances by providing a form containing the fields used by the deepnet, so you can easily set the input data and get an immediate response.

  • BigML batch predictions allow you to make simultaneous predictions for multiple instances. All you need is the deepnet you want to use to make predictions and a dataset containing the instances for which you want to obtain predictions.

  • You can configure your batch predictions output file settings.

  • You can download your deepnet to perform local predictions.

  • You can add descriptive information to your deepnets (name, description, tags, and category).

  • You can move your deepnets between projects.

  • You can share your deepnets with other people using the secret link.

  • You can stop your deepnet creation by deleting it.

  • You can permanently delete an existing deepnet.

\includegraphics[width=11cm]{images/deepnet/deepnet-workflow}
Figure 5.116 Deepnet Workflow