Prediction - Platform For AI - Alibaba Cloud Documentation Center

This topic describes the Prediction component provided by Machine Learning Designer. If your model is trained by using a traditional data mining component that does not have a paired prediction component, and you want to use the model to generate predictions, you can choose the Prediction component in most cases. The component uses the trained model and prediction data as input and generates the prediction results.

Configure the component

You can configure the component by using one of the following methods:

Method 1: Configure the component in Machine Learning Designer

Configure the component on the pipeline configuration tab of Machine Learning Designer in the Machine Learning Platform for AI console.

Tab	Parameter	Description
Fields Setting	Feature Columns	The feature columns that are selected from the input table for prediction. By default, all columns in the input table are selected.
	Reserved Columns	The columns that you want to reserve in the output table. We recommend that you add a label column to facilitate evaluation.
	Output Result Column	The result column in the output table.
	Output Score Column	The score column in the output table.
	Output Detail Column	The details column in the output table.
	Sparse Matrix	Specifies whether the input data is sparse. Sparse data is presented by using key-value pairs.
	KV Delimiter	The delimiter that is used to separate keys and values. By default, colons (:) are used.
	KV Pair Delimiter	The delimiter that is used to separate key-value pairs. By default, commas (,) are used.
Tuning	Cores	The number of cores. This parameter must be used together with the Memory Size per Core parameter. The value of this parameter must be a positive integer.
Tuning	Memory Size per Core	The memory size of each core. This parameter must be used with the Cores parameter. Unit: MB.

Method 2: Run Machine Learning Platform for AI commands

Configure the component parameters by using a Machine Learning Platform for AI command. You can use the SQL Script component to run Machine Learning Platform for AI commands. For more information, see SQL Script. The following table describes the parameters used in the command.

pai -name prediction
    -DmodelName=nb_model
    -DinputTableName=wpbc
    -DoutputTableName=wpbc_pred
    -DappendColNames=label;

Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	None
featureColNames	No	The feature columns that are selected from the input table for prediction. Separate multiple columns with commas (,).	All columns
appendColNames	No	The prediction columns that are selected from the input table and appended to the output table.	None
inputTablePartitions	No	The partitions that are selected from the input table for training. The following formats are supported: Partition_name=value name1=value1/name2=value2: multi-level partitions Note If you specify multiple partitions, separate them with commas (,).	Full table
outputTablePartition	No	The partitions whose results are contained in the output table.	None
resultColName	No	The column in the output table that contains the prediction results with the highest probabilities among all possible results.	prediction_result
scoreColName	No	The column in the output table that contains the highest probabilities of the prediction results.	prediction_score
detailColName	No	The details column in the output table that contains all possible results and their probabilities.	prediction_detail
enableSparse	No	Specifies whether the input data is sparse. Valid values: true and false.	false
itemDelimiter	No	The delimiter that is used to separate sparse key-value pairs.	,
kvDelimiter	No	The delimiter that is used to separate sparse keys and values.	:
modelName	Yes	The name of the input clustering model.	None
outputTableName	Yes	The name of the output table.	None
lifecycle	No	The lifecycle of the output table.	None
coreNum	No	The number of cores.	Automatically allocated
memSizePerCore	No	The memory size of each core. Unit: MB.	Automatically allocated

Example

Execute the following SQL statements to generate test data:

create table pai_rf_test_input as
select * from
(
select 1 as f0,2 as f1, "good" as class
union all
select 1 as f0,3 as f1, "good" as class
union all
select 1 as f0,4 as f1, "bad" as class
union all
select 0 as f0,3 as f1, "good" as class
union all
select 0 as f0,4 as f1, "bad" as class
)tmp;

Run the following command to build a model. The random forest algorithm is used in this example.

PAI -name randomforests
   -project algo_public
   -DinputTableName="pai_rf_test_input"
   -DmodelName="pai_rf_test_model"
   -DforceCategorical="f1"
   -DlabelColName="class"
   -DfeatureColNames="f0,f1"
   -DmaxRecordSize="100000"
   -DminNumPer="0"
   -DminNumObj="2"
   -DtreeNum="3";

Run the following command to submit the parameters configured for the Prediction component:

PAI -name prediction
    -project algo_public
    -DinputTableName=pai_rf_test_input
    -DmodelName=pai_rf_test_model
    -DresultColName=prediction_result
    -DscoreColName=prediction_score
    -DdetailColName=prediction_detail
    -DoutputTableName=pai_temp_2283_76333_1

View the output result table pai_temp_2283_76333_1, as shown in the following figure.
- prediction_result: the column that contains the prediction results with the highest probabilities among all possible results.
- prediction_score: the column that contains the probabilities of the prediction results.
  In this example, the prediction result can be good or bad, depending on whose probability is higher. The prediction_score column contains the highest probabilities.
- prediction_detail: the column that contains all possible results and their probabilities.