Creating an ML Model

After you've created a datasource, you are ready to create an ML model. If you use the Amazon Machine Learning console to create a model, you can choose to use the default settings or you customize your model by applying custom options.

Custom options include:

• Evaluation settings: You can choose to have Amazon ML reserve a portion of the input data to evaluate the predictive quality of the ML model. For information about evaluations, see Evaluating ML Models.

• A recipe: A recipe tells Amazon ML which attributes and attribute transformations are available for model training. For information about Amazon ML recipes, see Feature Transformations with Data Recipes.

• Training parameters: Parameters control certain properties of the training process and of the resulting ML model. For more information about training parameters, see Training Parameters.

To select or specify values for these settings, choose the Custom option when you use the Create ML Model wizard. If you want Amazon ML to apply the default settings, choose Default.

When you create an ML model, Amazon ML selects the type of learning algorithm it will use based on the attribute type of your target attribute. (The target attribute is the attribute that contains the "correct" answers.) If your target attribute is Binary, Amazon ML creates a binary classification model, which uses the logistic regression algorithm. If your target attribute is Categorical, Amazon ML creates a multiclass model, which uses a multinomial logistic regression algorithm. If your target attribute is Numeric, Amazon ML creates a regression model, which uses a linear regression algorithm.

Prerequisites

Before using the Amazon ML console to create an ML model, you need to create two datasources, one for training the model and one for evaluating the model. If you haven't created two datasources, see Step 2: Create a Training Datasource in the tutorial.

Creating an ML Model with Default Options

Choose the Default options, if you want Amazon ML to:

• Split the input data to use the first 70 percent for training and use the remaining 30 percent for evaluation

• Suggest a recipe based on statistics collected on the training datasource, which is 70 percent of the input datasource

• Choose default training parameters

To choose default options

1. In the Amazon ML console, choose Amazon Machine Learning, and then choose ML models.

2. On the ML models summary page, choose Create a new ML model.

3. On the Input data page, make sure that I already created a datasource pointing to my S3 data is selected.

4. In the table, choose your datasource, and then choose Continue.

5. On the ML model settings page, for ML model name, type a name for your ML model.

6. For Training and evaluation settings, make sure that Default is selected.

7. For Name this evaluation, type a name for the evaluation, and then choose Review. Amazon ML bypasses the rest of the wizard and takes you to the Review page.

8. Review your data, delete any tags copied from the datasource that you don't want applied to your model and evaluations, and then choose Finish.

Creating an ML Model with Custom Options

Customizing your ML model allows you to:

• Provide your own recipe. For information about how to provide your own recipe, see Recipe Format Reference.

• Choose training parameters. For more information about training parameters, see Training Parameters.

• Choose a training/evaluation splitting ratio other than the default 70/30 ratio or provide another datasource that you have already prepared for evaluation. For information about splitting strategies, see Splitting Your Data.

You can also choose the default values for any of these settings.

If you've already created a model using the default options and want to improve your model's predictive performance, use the Custom option to create a new model with some customized settings. For example, you might add more feature transformations to the recipe or increase the number of passes in the training parameter.

To create a model with custom options

1. In the Amazon ML console, choose Amazon Machine Learning, and then choose ML models.

2. On the ML models summary page, choose Create a new ML model.

3. If you have already created a datasource, on the Input data page, choose I already created a datasource pointing to my S3 data. In the table, choose your datasource, and then choose Continue.

   If you need to create a datasource, choose My data is in S3, and I need to create a datasource, choose Continue. You are redirected to the Create a Datasource wizard. Specify whether your data is in S3 or Redshift, then choose Verify. Complete the procedure for creating a datasource.

   After you have created a datasource, you are redirected to the next step in the Create ML Model wizard.

4. On the ML model settings page, for ML model name, type a name for your ML model.

5. In Select training and evaluation settings, choose Custom, and then choose Continue.

6. On the Recipe page, you can customize a recipe. If you don't want to customize a recipe, Amazon ML suggests one for you. Choose Continue.

7. On the Advanced settings page, specify the Maximum ML model Size, the Maximum number of data passes, the Shuffle type for training data, the Regularization type, and the Regularization amount. If you don't specify these, Amazon ML uses the default training parameters.

   For more information about these parameters and their defaults, see Training Parameters.

   Choose Continue.

8. On the Evaluation page, specify whether you want to evaluate the ML model immediately. If you don't want to evaluate the ML model now, choose Review.

   If you want to evaluate the ML model now:

   1. For Name this evaluation, type a name for the evaluation.

   2. For Select evaluation data, choose whether you want Amazon ML to reserve a portion of the input data for evaluation and, if you do, how you want to split the datasource, or choose to provide a different datasource for evaluation.

   3. Choose Review.

9. On the Review page, edit your selections, delete any tags copied from the datasource that you don't want applied to your model and evaluations, and then choose Finish.

After you have created the model, see Step 4: Review the ML Model's Predictive Performance and Set a Score Threshold.