This feature uses the "LeoBreiman" Random Forest algorithm, which is a supervised Machine Learning method. It is a forest-based classification method when creating a model and performing predictions for a categorical variable. It can be used to answer questions such as yes or no, occur or not occur, etc., for the discriminant type.

The data Training Procedure based on the forest classification method can obtain the forest model according to the data characteristics, and then use it for prediction.

When creating a forest-based classification training task, you need to set the following parameters:

• Training Dataset: required parameter. The Dataset to be trained accesses Connection Info, including Data Type, Connect Parameter, Dataset name, etc. You can connect HBase data, dsf data, and Local Data. Use '--key = value' to set multiple values using ' 'space Separated. If the connection HBase data is -- providerType = hbase -- hbase. Zookeepers = 192.168.12.34: 2181 --hbase.catalog=demo --dataset=dltb; The connection dsf data is --providerType=dsf --path =hdfs://ip:9000/dsfdata ; Local Data is --providerType=dsf --path=/home/dsfdata

• Data Query Conditions: optional parameter; the specified data can be filtered out for corresponding analysis according to the Query Conditions; attribute conditions and Spatial Query are supported. E.g. SmID < 100 and BBOX(the_geom, 120,30,121,31)。

• Explanatory Fields: a required parameter, the field of the explanatory variable. Enter one or more explanatory fields of the training Dataset as the independent variables of the model, which can help predict the category.

• Modeling field: a required parameter, which is used to train the field of the model, that is, the dependent variable. This field corresponds to a known (trained) value of a variable that will be used to make predictions at unknown locations.

• Number of Trees: Optional parameter, the number of trees to be created in the forest model. Increasing the number of trees will generally produce more accurate model predictions, but will increase the time for model computation. Range of values & gt; 0, default 100.

• Depth of the tree: An optional parameter, or the maximum number of partitions made into the tree. The value range is 0-30 and the default value is 30. If you use a larger maximum depth, more divisions will be created, which may increase the likelihood of overfitting the model.

• Leaf Node Splitting Threshold: Optional parameter, the minimum number of observations required to retain a leaf (i.e., a terminal node on a tree that is not further split). The value range is 0, and the default value is 1. For very large data, increasing these values will reduce the runtime of the tool.

• Percent of data used per decision tree: Optional parameter that specifies the percentage of elements to be used for each decision tree. The value range is 0-1.0. The default value is 1.0, which means 100% of the data. Use a lower percentage of Input Data for each decision tree: This speeds up the tool for large Datasets.

• Distance explanatory variable Dataset: optional parameter, supports point, line and Region Dataset, calculates the Closest distance between the elements of the given Dataset and the elements in the training Dataset, and automatically creates a list of explanatory variables.

• Model Save Directory: optional parameter; save the model with good Training Result to this address. An empty value indicates that the Model will not be saved.

After executing the training task, the following Result Parameter is output:

• fbModelCharacteristics: The attributes of the forest model, including the number of trees numTrees, the minimum sample number leafSize of the leaf nodes of the tree, the maximum depth of the tree maxTreesDepth, and the minimum depth of the tree minTreesDepth.

• Variable: The Field array of the forest model, which refers to the field of the independent variable in the training model.

• Variable Importances: The field importance refers to the degree of influence of the respective variable characteristics on the dependent variable.

• F1 Score: weighted f1-measure.

• Accuracy: Weighted accuracy.

• WeightedPrecision: The weighted precision.

• WeightedRecall: The weighted recall.

• classification Diagnostics: classification result diagnostics. Including f1Score, precision, recall, true positive rate and false positive rate for each classification category.