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}](images/img-0564.png)