Amazon SageMaker Canvas uses different advanced performance metrics to give you a sense of how well your model performed. The advanced metrics that SageMaker Canvas shows you depend on whether your model performs numeric, categorical, or time series forecasting predictions on your data.

Numeric prediction refers to the mathematical concept of regression. When your Target column has values that can be measured, such as yearly revenue or the number of items sold by a department store, Amazon SageMaker Canvas builds a model on your data using regression. For more information about regression, see Metrics for numeric prediction.

Categorical prediction, such as 2 category prediction or 3 category prediction, refers to the mathematical concept of classification. Categorical prediction can be performed on data that can be put into a category:

• The colors on a color wheel

• Instances where the data is either a 0 or 1

• Instances where the data is either a Yes or a No.

• A list of responses to a survey question.

Time series forecasting refers to making predictions that vary over time. You can perform time series forecasts on data with timestamps that correlate to a value you want to predict. For example, you can make a time series forecast that takes daily sales data and makes sales predictions for the next month.

SageMaker Canvas uses confusion matrices to help you visualize when a model makes predictions correctly.

The following image is an example of a confusion matrix for 2 categories. The following image is an example of a confusion matrix for 3+ categories. ## Metrics for numeric prediction

The following defines the advanced metrics for numeric prediction in Amazon SageMaker Canvas and gives you information about how you can use them.

• R2 – The percentage of the difference in the target column that can be explained by the input column.

• MAE – Mean absolute error. On average, the prediction for the target column is +/- {MAE} from the actual value.

• MAPE – Mean absolute percent error. On average, the prediction for the target column is +/- {MAPE} % from the actual value

• RMSE – Root Mean Square Error. The standard deviation of the errors.

The following image shows a graph of the residuals or errors. The horizontal line indicates an error of 0 or a perfect prediction. The blue dots are the errors. Their distance from the horizontal line indicates the magnitude of the errors. The following image shows an error density plot. ## Metrics for categorical prediction

The following defines the advanced metrics for categorical prediction in Amazon SageMaker Canvas and gives you information about how you can use them.

• Missing – A missing value contains no content or is non-existent. Missing values are automatically inferred.

• Mismatched – A mismatched value has a different data type from the type specified for its column. SageMaker Canvas categorizes these values as missing and infers values for them.

• Unique – The number and percentage of values that are unique.

• Target correlation – A value between -1 and 1 that represents strength of the linear relationship between a column and the target column. `0` represents no detectable relationship. `1` represents a strong positive relationship. `-1` represents a strong negative relationship.

• Column impact – Identifies the relative impact of the column in predicting the target column.

The following is a list of available metrics for binary classification.

• F1 – A balanced measure of accuracy that takes class balance into account.

• Accuracy – The percentage of correct predictions.

• Precision – Of all the times that {category-1} was predicted, the prediction was correct {precision}% of the time.

• Recall – The model correctly predicted {recall}% to be {category-1} when {target_column} was actually {category-1}.

• AUC – A value between 0 and 1 that indicates how well your model is able to separate the categories in your dataset. A value of 1 indicates that it was able to separate the categories perfectly.

The following is a list of available metrics for multi-classification.

• F1 – A balanced measure of accuracy that takes class balance into account.

• Accuracy – The percentage of correct predictions.

• Precision – Of all the times that {category-1} was predicted, the prediction was correct {precision}% of the time.

• Recall – The model correctly predicted {recall}% to be {category-1} when {target_column} was actually {category-1}.

• AUC – A value between 0 and 1 that indicates how well your model is able to separate the categories in your dataset. A value of 1 indicates that it was able to separate the categories perfectly.

• Average F1 – The F1 averaged for all categories.

• Average Accuracy – The percentage of correct predictions out of all the predictions that are made.

• Average Precision – The precision averaged for all categories.

• Average Recall – The recall averaged for all categories.

• Average AUC – The AUC averaged for all categories.

## Metrics for time series forecasts

The following defines the advanced metrics for time series forecasts in Amazon SageMaker Canvas and gives you information about how you can use them.

• Average Weighted Quantile Loss (wQL) – Evaluates the forecast by averaging the accuracy at the P10, P50, and P90 quantiles. A lower value indicates a more accurate model.

• Weighted Absolute Percent Error (WAPE) – The sum of the absolute error normalized by the sum of the absolute target, which measure the overall deviation of forecasted values from observed values. A lower value indicates a more accurate model, where WAPE = 0 is a model with no errors.

• Root Mean Square Error (RMSE) – The square root of the average squared errors. A lower RMSE indicates a more accurate model, where RMSE = 0 is a model with no errors.

• Mean Absolute Percent Error (MAPE) – The percentage error (percent difference of the mean forecasted value versus the actual value) averaged over all time points. A lower value indicates a more accurate model, where MAPE = 0 is a model with no errors.

• Mean Absolute Scaled Error (MASE) – The mean absolute error of the forecast normalized by the mean absolute error of a simple baseline forecasting method. A lower value indicates a more accurate model, where MASE < 1 is estimated to be better than the baseline and MASE > 1 is estimated to be worse than the baseline.