The GBDT Binary Classification component is used to set a threshold. If a feature value is greater than the threshold, the feature is a positive example. Otherwise, the feature is a negative example.
Configure the component
You can use one of the following methods to configure the GBDT Binary Classification component.
Method 1: Configure the component on the pipeline page
You can configure the parameters of the GBDT Binary Classification component on the pipeline page of Machine Learning Designer of Platform for AI (PAI). Machine Learning Designer is formerly known as Machine Learning Studio. The following table describes the parameters.
Tab | Parameter | Description |
Fields Setting | Feature Columns | The feature columns that are selected from the input table for training. The columns of the DOUBLE and BIGINT types are supported. Note A maximum of 800 feature columns can be selected. |
Label Column | The label column. Only the column of the BIGINT type is supported. | |
Stratified Column | The columns of the DOUBLE and BIGINT types are supported. By default, the full table is a group. | |
Parameters Setting | Metric Type | The metric type. Valid values: NDCG and DCG. |
Decision Tree Quantity | The number of trees. Valid values: 1 to 10000. | |
Learning Rate | The learning rate. Valid values: (0,1). | |
Rate of Samples for Training | The proportion of samples that are selected for training. Valid values: (0,1]. | |
Ratio of Features for Training | The proportion of features that are selected for training. Valid values: (0,1]. | |
Maximum Leaf Quantity | The maximum number of leaf nodes on each tree. Valid values: 1 to 1000. | |
Testing Data Ratio | The proportion of data that is selected for testing. Valid values: [0,1). | |
Maximum Decision Tree Depth | The maximum depth of each tree. Valid values: 1 to 100. | |
Minimum Number of Samples on a Leaf Node | The minimum number of samples on each leaf node. Valid values: 1 to 1000. | |
Random Seed | The random seed. Valid values: [0,10]. | |
Maximum Feature Split Times | The maximum number of splits of each feature. Valid values: 1 to 1000. | |
Tuning | Number of Cores | The number of cores. The system automatically allocates cores used for training based on the volume of input data. |
Memory | The memory size of each core. The system automatically allocates the memory based on the volume of input data. The memory size of each core. Valid values: 1024 to 64 × 1024. Unit: MB. |
Method 2: Use PAI commands
Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see SQL Script.
PAI -name gbdt_lr
-project algo_public
-DfeatureSplitValueMaxSize="500"
-DrandSeed="0"
-Dshrinkage="0.5"
-DmaxLeafCount="32"
-DlabelColName="y"
-DinputTableName="bank_data_partition"
-DminLeafSampleCount="500"
-DgroupIDColName="nr_employed"
-DsampleRatio="0.6"
-DmaxDepth="11"
-DmodelName="xlab_m_GBDT_LR_21208"
-DmetricType="2"
-DfeatureRatio="0.6"
-DinputTablePartitions="pt=20150501"
-DtestRatio="0.0"
-DfeatureColNames="age,previous,cons_conf_idx,euribor3m"
-DtreeCount="500"
Parameter | Required | Description | Default value |
inputTableName | Yes | The name of the input table. | N/A |
featureColNames | No | The feature columns that are selected from the input table for training. | All columns of numeric data types |
labelColName | Yes | The name of the label column in the input table. | N/A |
inputTablePartitions | No | The partitions that are selected from the input table for training. Specify this parameter in one of the following formats:
Note If you specify multiple partitions, separate these partitions with commas (,). | All partitions |
modelName | Yes | The name of the output model. | N/A |
outputImportanceTableName | No | The name of the table that provides feature importance. | N/A |
groupIDColName | No | The name of the group column. | Full table |
lossType | No | The type of the loss function is 4: LOG_LIKELIHOOD. | 4 |
metricType | No | The metric type. Valid values:
| 0 |
treeCount | No | The number of trees. Valid values: 1 to 10000. | 500 |
shrinkage | No | The learning rate. Valid values: (0,1). | 0.05 |
maxLeafCount | No | The maximum number of leaf nodes on each tree. Valid values: 1 to 1000. | 32 |
maxDepth | No | The maximum depth of each tree. Valid values: 1 to 100. | 10 |
minLeafSampleCount | No | The minimum number of samples on each leaf node. Valid values: 1 to 1000. | 500 |
sampleRatio | No | The proportion of samples that are selected for training. Valid values: (0,1]. | 0.6 |
featureRatio | No | The proportion of features that are selected for training. Valid values: (0,1]. | 0.6 |
tau | No | The Tau parameter for the GBRank loss function. Valid values: [0,1]. | 0.6 |
p | No | The p parameter for the GBRank loss function. Valid values: [1,10]. | 1 |
randSeed | No | The random seed. Valid values: [0,10]. | 0 |
newtonStep | No | Specifies whether to use Newton's method. Valid values: 0 and 1. | 1 |
featureSplitValueMaxSize | No | The maximum number of splits of each feature. Valid values: 1 to 1000. | 500 |
lifecycle | No | The lifecycle of the output table. Unit: days. | N/A |
By default, the loss function types of gradient boosting decision tree (GBDT) and those of GBDT_LR are different. By default, GBDT uses regression loss:mean squared error loss as its loss function, and GBDT_LR uses logistic regression loss as its loss function. Therefore, you do not need to configure a loss function for GBDT_LR.
The feature column, label column, and stratification column of GBDT must be of numeric data types.
You must specify the objective reference value for the Prediction component to generate a receiver operating characteristic (ROC) curve.
Example
Execute the following SQL statements to generate training data:
drop table if exists gbdt_lr_test_input; create table gbdt_lr_test_input as select * from ( select cast(1 as double) as f0, cast(0 as double) as f1, cast(0 as double) as f2, cast(0 as double) as f3, cast(0 as bigint) as label from dual union all select cast(0 as double) as f0, cast(1 as double) as f1, cast(0 as double) as f2, cast(0 as double) as f3, cast(0 as bigint) as label from dual union all select cast(0 as double) as f0, cast(0 as double) as f1, cast(1 as double) as f2, cast(0 as double) as f3, cast(1 as bigint) as label from dual union all select cast(0 as double) as f0, cast(0 as double) as f1, cast(0 as double) as f2, cast(1 as double) as f3, cast(1 as bigint) as label from dual union all select cast(1 as double) as f0, cast(0 as double) as f1, cast(0 as double) as f2, cast(0 as double) as f3, cast(0 as bigint) as label from dual union all select cast(0 as double) as f0, cast(1 as double) as f1, cast(0 as double) as f2, cast(0 as double) as f3, cast(0 as bigint) as label from dual ) a;
The following training data table gbdt_lr_test_input is generated.
f0
f1
f2
f3
label
1.0
0.0
0.0
0.0
0
0.0
0.0
1.0
0.0
1
0.0
0.0
0.0
1.0
1
0.0
1.0
0.0
0.0
0
1.0
0.0
0.0
0.0
0
0.0
1.0
0.0
0.0
0
Run the following PAI command to submit the training parameters configured for the GBDT Binary Classification component:
drop offlinemodel if exists gbdt_lr_test_model; PAI -name gbdt_lr -project algo_public -DfeatureSplitValueMaxSize="500" -DrandSeed="1" -Dshrinkage="1" -DmaxLeafCount="30" -DlabelColName="label" -DinputTableName="gbdt_lr_test_input" -DminLeafSampleCount="1" -DsampleRatio="1" -DmaxDepth="10" -DmodelName="gbdt_lr_test_model" -DmetricType="0" -DfeatureRatio="1" -DtestRatio="0" -DfeatureColNames="f0,f1,f2,f3" -DtreeCount="5"
Run the following PAI command to submit the parameters configured for the Prediction component:
drop table if exists gbdt_lr_test_prediction_result; PAI -name prediction -project algo_public -DdetailColName="prediction_detail" -DmodelName="gbdt_lr_test_model" -DitemDelimiter="," -DresultColName="prediction_result" -Dlifecycle="28" -DoutputTableName="gbdt_lr_test_prediction_result" -DscoreColName="prediction_score" -DkvDelimiter=":" -DinputTableName="gbdt_lr_test_input" -DenableSparse="false" -DappendColNames="label";
View the prediction result table gbdt_lr_test_prediction_result.
label
prediction_result
prediction_score
prediction_detail
0
0
0.9984308925552831
{"0": 0.9984308925552831, "1": 0.001569107444716943}
0
0
0.9984308925552831
{"0": 0.9984308925552831, "1": 0.001569107444716943}
1
1
0.9982721832240973
{"0": 0.001727816775902724, "1": 0.9982721832240973}
1
1
0.9982721832240973
{"0": 0.001727816775902724, "1": 0.9982721832240973}
0
0
0.9984308925552831
{"0": 0.9984308925552831, "1": 0.001569107444716943}
0
0
0.9984308925552831
{"0": 0.9984308925552831, "1": 0.001569107444716943}