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.
- Collect your input data
- Open the AutoAI tool
- Specify details of your model and training data and launch AutoAI
- View the results
Collect your input data
Collect your input data in a CSV file or files. Where possible, AutoAI will transform the data and impute missing values.
- You can use the IBM Watson Studio Data Refinery tool to prepare and shape your data.
- Your data can reside in multiple files if you plan to use AutoAI to join the files into a single data set for training the experiment. The files must share common columns, or keys, to join the data. For details, see Joining data sources.
Open the AutoAI tool
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 and 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 click Create.
- Choose data from your project or upload it from your file system, then press Continue. Data must be in a CSV file. You can click the Preview icon after the data source name 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 3 or more values, and regression if the target column is a continuous numeric variable. You can override this selection.
- AutoAI chooses a default metric for optimizing. For example, the default metric for a binary classification model is Accuracy.
- 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:
- 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, where you can:
- change the model type. AutoAI selects a model type that best suits a sampling of the data, but you can override it. For example, if the sample data for the prediction column contains only two types of values, AutoAI will choose binary classification as the model type. If you know there are more than two values in the column, you can override the setting and choose multiclass classification instead. For binary classification models you can also edit the positive class.
- change the metric to be optimised for the experiment. Note: For a binary classification experiment, if you change the metric to Precision, Average Precision, Recall, or F1, a Positive Class is required. Confirm that the Positive Class is correct or the experiment might generate inaccurate results.
- optionally specify which algorithms AutoAI should consider for pipeline creation. Only checked algorithms will be considered during the model selection phase of the experiment.
- change the number of algorithms to use to create pipelines. By default, AutoAI will choose the top two performing algorithms of the ones it considers, and use those algorithms to generate 8 pipelines that you can view and compare, but you can change the number from 1 to 4. For example, if you select 3 algorithms, AutoAI will identify the top three performing algorithms and use them to generate a total of 12 pipelines that you can view, compare, and save as models. Note that more pipelines will increase the training time for the experiment and use more resources.
- Runtime settings, where you can review experiment settings.
- Data source, where you can adjust:
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.
Watch this video to see how to run an AutoAI to build a binary classification model.
Watch this video to see how to run an AutoAI to build a multiclass classification model.