Configuring an AutoML experiment
If you were asked to train a model to make predictions against a dataset, you would need to do a couple of things, including normalizing the dataset, splitting it into train and validation data, running multiple experiments to understand which algorithm is performing best against the dataset, and then finetuning the best model. Automated machine learning shortens this process by fully automating the time-consuming, iterative tasks. It allows all users, from normal PC users to experienced data scientists, to build multiple machine learning models against a target dataset and select the model that performs the best, based on a metric you select.
This process consists of the following steps:
- Preparing the experiment: Select the dataset you are going to use for training, select the column that you are trying to predict, and configure the experiment's parameters. This is the configuration phase you will read about in this section.
- Data...
