Building an AutoAI model
AutoAI automatically prepares data, applies algorithms, and attempts to build model pipelines best suited for your data and use case. This topic describes how to generate the model pipelines.
Follow these steps to upload data and have AutoAI create the best model for your data and use case.
- Prepare your training data
- Open the AutoAI tool
- Create or open a project
- Specify details of your model and training data and launch AutoAI
- View the results
Prepare your training data
Collect your model data in a CSV file that is less than 100MB. Where possible, AutoAI will transform the data and impute missing values.
Note: You can use the IBM Watson Studio Data Refinery tool to prepare and shape your data.
Create or open a project
For your convenience, your AutoAI model creation uses the default storage associated with your project to store your data and to save model results, so you do not have to set up any separate repositories.
- Open a project or create a new one.
- If you are prompted to select a region, choose the US South region.
- If you don’t already have the required Watson Machine Learning service, follow the prompts to create new service instances.
- (Optional) Upload your data training file in CSV format as a data asset for the project.
Open the AutoAI tool
- Click Add to project.
- Click AUTOAI EXPERIMENT.
Note: After you create an AutoAI asset it will display on the Assets page for your project in the AutoAI experiment section, so you can return to it.
Specify details of your experiment
- Specify a name and description for your experiment.
- Select a machine learning service instance and a compute configuration and click Create.
- Choose data from your project or upload it from your file system, then press Continue. Data must be in a CSV file and must be smaller than 100MB. Click the Preview icon to review your data.
- Choose the Column to predict for the data you want the experiment to predict.
- Based on analyzing a subset of the data set, AutoAI chooses a default model type: binary classification, multiclass classification, or regression. Binary is selected if the target column has two possible values, multiclass if it has a discrete set of possible values, for example 5 or 10, and regression if there is an infinite number of possible values. You can override this selection.
- AutoAI chooses a default metric for optimizing. For example, the default metric for a binary classification model is ROC AUC, which balances precision, accuracy, and recall.
- By default, ten percent of the training data is held out to test the performance of the model.
- (Optional) Click Experiment settings to view or customize options for your AutoAI run. To edit the settings for your experiment, click:
- Runtime settings, where you can review experiment settings or change the compute configuration for the experiment.
- Data source, where you can adjust:
- whether to subsample data. If you have a large data set, you can choose to train with a representative sample of the data to speed up pipeline creation. You can specify whether subsampling should be done by a percentage of the training data or by a specified number of rows.
- the percentage of training data vs holdout data. Training data is used to train the model, and holdout data is withheld from training the model and used to measure the performance of the model.
- columns to include. You can choose to include columns with data that supports the prediction column, and exclude irrelevant columns to speed up pipeline performance.
- Prediction settings, to optionally specify which algorithms AutoAI should consider for pipeline creation. Only checked algorithms will be considered during the model selection phase of the experiment. For binary classification models you can also edit the positive class.
Click Run Experiment to begin model pipeline creation.
An infographic shows you the creation of pipelines for your data. The duration of this phase depends on the size of your data set. A notification message informs you if the processing time will be brief or require more time. You can work in other parts of the product while the pipelines build.
Hover over nodes in the infographic to explore the factors that pipelines share as well as their unique properties. You can see the factors that pipelines share as well as the properties that make a pipeline unique. For a guide to the data in the infographic, click the Legend tab in the information pane. Or, to see a different view of the pipeline creation, click the Experiment details tab of the notification pane, then click Switch views to view the progress map. In either view, click a pipeline node to view the associated pipeline in the leaderboard.
View the results
When the pipeline generation process completes, you can view the leading model candidates and evaluate them before saving a pipeline as a model.
Follow the steps in Selecting an AutoAI model for details on how to evaluate the pipelines as model candidates, then save a model.
This video shows you how to run an AutoAI to build a binary classification model.
This video shows you how to run an AutoAI to build a multiclass classification model.