# Build a model


The following sections show you how to build a model for each of the main types of custom models.
+ To build numeric prediction, 2 category prediction, or 3\$1 category prediction models, see [Build a custom numeric or categorical prediction model](#canvas-build-model-numeric-categorical).
+ To build single-label image prediction models, see [Build a custom image prediction model](#canvas-build-model-image).
+ To build multi-category text prediction models, see [Build a custom text prediction model](#canvas-build-model-text).
+ To build time series forecasting models, see [Build a time series forecasting model](#canvas-build-model-forecasting).

**Note**  
If you encounter an error during post-building analysis that tells you to increase your quota for `ml.m5.2xlarge` instances, see [Request a Quota Increase](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-requesting-quota-increases.html).

## Build a custom numeric or categorical prediction model


Numeric and categorical prediction models support both **Quick builds** and **Standard builds**.

To build a numeric or categorical prediction model, use the following procedure:

1. Open the SageMaker Canvas application.

1. In the left navigation pane, choose **My models**.

1. Choose **New model**.

1. In the **Create new model** dialog box, do the following:

   1. Enter a name in the **Model name** field.

   1. Select the **Predictive analysis** problem type.

   1. Choose **Create**.

1. For **Select dataset**, select your dataset from the list of datasets. If you haven’t already imported your data, choose **Import** to be directed through the import data workflow.

1. When you’re ready to begin building your model, choose **Select dataset**.

1. On the **Build** tab, for the **Target column** dropdown list, select the target for your model that you would like to predict.

1. For **Model type**, Canvas automatically detects the problem type for you. If you want to change the type or configure advanced model settings, choose **Configure model**.

   When the **Configure model** dialog box opens, do the following:

   1. For **Model type**, choose the model type that you want to build.

   1. After you choose the model type, there are additional **Advanced settings**. For more information about each of the advanced settings, see [Advanced model building configurations](canvas-advanced-settings.md). To configure the advanced settings, do the following:

      1. (Optional) For the **Objective metric** dropdown menu, select the metric that you want Canvas to optimize while building your model. If you don’t select a metric, Canvas chooses one for you by default. For descriptions of the available metrics, see [Metrics reference](canvas-metrics.md).

      1. For **Training method**, choose **Auto**, **Ensemble**, or **Hyperparameter optimization (HPO) mode**.

      1. For **Algorithms**, select the algorithms that you want to include for building model candidates.

      1. For **Data split**, specify in percentages how you want to split your data between the **Training set** and the **Validation set**. The training set is used for building the model, while the validation set is used for testing accuracy of model candidates.

      1. For **Max candidates and runtime**, do the following:

         1. Set the **Max candidates** value, or the maximum number of model candidates that Canvas can generate. Note that **Max candidates** is only available in HPO mode.

         1. Set the hour and minute values for **Max job runtime**, or the maximum amount of time that Canvas can spend building your model. After the maximum time, Canvas stops building and selects the best model candidate.

   1. After configuring the advanced settings, choose **Save**.

1. Select or deselect columns in your data to include or drop them from your build.
**Note**  
If you make batch predictions with your model after building, Canvas adds dropped columns to your prediction results. However, Canvas does not add the dropped columns to your batch predictions for time series models.

1. (Optional) Use the visualization and analytics tools that Canvas provides to visualize your data and determine which features you might want to include in your model. For more information, see [Explore and analyze your data](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-explore-data.html).

1. (Optional) Use data transformations to clean, transform, and prepare your data for model building. For more information, see [ Prepare your data with advanced transformations](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-prepare-data.html). You can view and remove your transforms by choosing **Model recipe** to open the **Model recipe** side panel.

1. (Optional) For additional features such as previewing the accuracy of your model, validating your dataset, and changing the size of the random sample that Canvas takes from your dataset, see [Preview your model](canvas-preview-model.md).

1. After reviewing your data and making any changes to your dataset, choose **Quick build** or **Standard build** to begin a build for your model. The following screenshot shows the **Build** page and the **Quick build** and **Standard build** options.  
![\[The Build page for a 2 category model showing the Quick build and Standard build options.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/build-page-tabular-quick-standard-options.png)

After your model begins building, you can leave the page. When the model shows as **Ready** on the **My models** page, it’s ready for analysis and predictions.

## Build a custom image prediction model


Single-label image prediction models support both **Quick builds** and **Standard builds**.

To build a single-label image prediction model, use the following procedure:

1. Open the SageMaker Canvas application.

1. In the left navigation pane, choose **My models**.

1. Choose **New model**.

1. In the **Create new model** dialog box, do the following:

   1. Enter a name in the **Model name** field.

   1. Select the **Image analysis** problem type.

   1. Choose **Create**.

1. For **Select dataset**, select your dataset from the list of datasets. If you haven’t already imported your data, choose **Import** to be directed through the import data workflow.

1. When you’re ready to begin building your model, choose **Select dataset**.

1. On the **Build** tab, you see the **Label distribution** for the images in your dataset. The **Model type** is set to **Single-label image prediction**.

1. On this page, you can preview your images and edit the dataset. If you have any unlabeled images, choose **Edit dataset** and [Assign labels to unlabeled images](canvas-edit-image.md#canvas-edit-image-assign). You can also perform other tasks when you [Edit an image dataset](canvas-edit-image.md), such as renaming labels and adding images to the dataset.

1. After reviewing your data and making any changes to your dataset, choose **Quick build** or **Standard build** to begin a build for your model. The following screenshot shows the **Build** page of an image prediction model that is ready to be built.  
![\[The Build page for a single-label image prediction model.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/build-page-image-model.png)

After your model begins building, you can leave the page. When the model shows as **Ready** on the **My models** page, it’s ready for analysis and predictions.

## Build a custom text prediction model


Multi-category text prediction models support both **Quick builds** and **Standard builds**.

To build a text prediction model, use the following procedure:

1. Open the SageMaker Canvas application.

1. In the left navigation pane, choose **My models**.

1. Choose **New model**.

1. In the **Create new model** dialog box, do the following:

   1. Enter a name in the **Model name** field.

   1. Select the **Text analysis** problem type.

   1. Choose **Create**.

1. For **Select dataset**, select your dataset from the list of datasets. If you haven’t already imported your data, choose **Import** to be directed through the import data workflow.

1. When you’re ready to begin building your model, choose **Select dataset**.

1. On the **Build** tab, for the **Target column** dropdown list, select the target for your model that you would like to predict. The target column must have a binary or categorical data type, and there must be at least 25 entries (or rows of data) for each unique label in the target column.

1. For **Model type**, confirm that the model type is automatically set to **Multi-category text prediction**.

1. For the training column, select your source column of text data. This should be the column containing the text that you want to analyze.

1. Choose **Quick build** or **Standard build** to begin building your model. The following screenshot shows the **Build** page of a text prediction model that is ready to be built.  
![\[The Build page for a multi-category text prediction model.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/build-page-text-model.png)

After your model begins building, you can leave the page. When the model shows as **Ready** on the **My models** page, it’s ready for analysis and predictions.

## Build a time series forecasting model


Time series forecasting models support both **Quick builds** and **Standard builds**.

To build a time series forecasting model, use the following procedure:

1. Open the SageMaker Canvas application.

1. In the left navigation pane, choose **My models**.

1. Choose **New model**.

1. In the **Create new model** dialog box, do the following:

   1. Enter a name in the **Model name** field.

   1. Select the **Time series forecasting** problem type.

   1. Choose **Create**.

1. For **Select dataset**, select your dataset from the list of datasets. If you haven’t already imported your data, choose **Import** to be directed through the import data workflow.

1. When you’re ready to begin building your model, choose **Select dataset**.

1. On the **Build** tab, for the **Target column** dropdown list, select the target for your model that you would like to predict.

1. In the **Model type** section, choose **Configure model**.

1. The **Configure model** box opens. For the **Time series configuration** section, fill out the following fields:

   1. For **Item ID column**, choose a column in your dataset that uniquely identifies each row. The column should have a data type of `Text`.

   1. (Optional) For **Group column**, choose one or more categorical columns (with a data type of `Text`) that you want to use for grouping your forecasting values.

   1. For **Time stamp column**, select the column with timestamps (in datetime format). For more information about the accepted datetime formats, see [Time Series Forecasts in Amazon SageMaker Canvas](canvas-time-series.md).

   1. For the **Forecast length** field, enter the period of time for which you want to forecast values. Canvas automatically detects the units of time in your data.

   1. (Optional) Turn on the **Use holiday schedule** toggle to select a holiday schedule from various countries and make your forecasts with holiday data more accurate.

1. In the **Configure model** box, there are additional settings in the **Advanced** section. For more information about each of the advanced settings, see [Advanced model building configurations](canvas-advanced-settings.md). To configure the **Advanced** settings, do the following:

   1. For the **Objective metric** dropdown menu, select the metric that you want Canvas to optimize while building your model. If you don’t select a metric, Canvas chooses one for you by default. For descriptions of the available metrics, see [Metrics reference](canvas-metrics.md).

   1. If you’re running a standard build, you’ll see the **Algorithms** section. This section is for selecting the time series forecasting algorithms that you’d like to use for building your model. You can select a subset of the available algorithms, or you can select all of them if you aren’t sure which ones to try.

      When you run your standard build, Canvas builds an ensemble model that combines all of the algorithms together to optimize prediction accuracy.
**Note**  
If you’re running a quick build, Canvas uses a single tree-based learning algorithm to train your model, and you don’t have to select any algorithms.

   1. For **Forecast quantiles**, enter up to 5 comma-separated quantile values to specify the upper and lower bounds of your forecast.

   1. After configuring the **Advanced** settings, choose **Save**.

1. Select or deselect columns in your data to include or drop them from your build.
**Note**  
If you make batch predictions with your model after building, Canvas adds dropped columns to your prediction results. However, Canvas does not add the dropped columns to your batch predictions for time series models.

1. (Optional) Use the visualization and analytics tools that Canvas provides to visualize your data and determine which features you might want to include in your model. For more information, see [Explore and analyze your data](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-explore-data.html).

1. (Optional) Use data transformations to clean, transform, and prepare your data for model building. For more information, see [ Prepare your data with advanced transformations](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-prepare-data.html). You can view and remove your transforms by choosing **Model recipe** to open the **Model recipe** side panel.

1. (Optional) For additional features such as previewing the accuracy of your model, validating your dataset, and changing the size of the random sample that Canvas takes from your dataset, see [Preview your model](canvas-preview-model.md).

1. After reviewing your data and making any changes to your dataset, choose **Quick build** or **Standard build** to begin a build for your model.

After your model begins building, you can leave the page. When the model shows as **Ready** on the **My models** page, it’s ready for analysis and predictions.