Overview
This section is where the targeted elements are defined, and where the learning data is separated into positive (mineralized) and negative (unmineralized) examples. The system uses learning points during training to recognize patterns in the data and make predictions. Each learning point helps improve the system's understanding of the underlying information.
Read on for more context and explanations about what this step entails.
Key Concepts by Parameter
Parameter: Target Element(s)
The values in this drop-down menu are determined by your Learning Points shapefile attribute file.
You can set multiple Target Elements and Thresholds within one Prediction Map.
When targeting more than one element, positive values will be created for samples that meet at least one of the thresholds.
Parameter: Threshold
The threshold indicates the minimum grade the element must meet to be represented as a positive learning data point.
Element(s) that do not reach the defined grade threshold will be represented as a negative learning point.
The minimum and maximum values that can be used as a threshold will be displayed immediately under the Target Element / Threshold inputs. These are based on the lowest and highest values in your Learning Points shapefile attribute file.
Target elements and thresholds are added once the ⨁ button is clicked, and the target is listed below:
Parameter: Elevation Field
The values in this drop-down menu are populated from the column headers in your Learning Points shapefile attribute file. The target elevation selected here must match the column header that contains the Z coordinates of your learning points. This will be used to predict the 3D component of the prospectivity score.
Still have questions?
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