Train the decision tree using the specific training data
| C# | Visual Basic | Visual C++ |
public static bool CvDTreeTrain( IntPtr model, IntPtr trainData, DATA_LAYOUT_TYPE tflag, IntPtr responses, IntPtr varIdx, IntPtr sampleIdx, IntPtr varType, IntPtr missingMask, MCvDTreeParams param )
Public Shared Function CvDTreeTrain ( _ model As IntPtr, _ trainData As IntPtr, _ tflag As DATA_LAYOUT_TYPE, _ responses As IntPtr, _ varIdx As IntPtr, _ sampleIdx As IntPtr, _ varType As IntPtr, _ missingMask As IntPtr, _ param As MCvDTreeParams _ ) As Boolean
public: static bool CvDTreeTrain( IntPtr model, IntPtr trainData, DATA_LAYOUT_TYPE tflag, IntPtr responses, IntPtr varIdx, IntPtr sampleIdx, IntPtr varType, IntPtr missingMask, MCvDTreeParams param )
- model (IntPtr)
- The Decision Tree model
- trainData (IntPtr)
- The training data. A 32-bit floating-point, single-channel matrix, one vector per row
- tflag (DATA_LAYOUT_TYPE)
- The data layout type of the train data
- responses (IntPtr)
- A floating-point matrix of the corresponding output vectors, one vector per row.
- varIdx (IntPtr)
- Can be IntPtr.Zero if not needed. When specified, identifies variables (features) of interest. It is a Matrix>int< of nx1
- sampleIdx (IntPtr)
- Can be IntPtr.Zero if not needed. When specified, identifies samples of interest. It is a Matrix>int< of nx1
- varType (IntPtr)
- The types of input variables
- missingMask (IntPtr)
- Can be IntPtr.Zero if not needed. When specified, it is an 8-bit matrix of the same size as trainData, is used to mark the missed values (non-zero elements of the mask)
- param (MCvDTreeParams)
- The parameters for training the decision tree
[Missing <returns> documentation for M:Emgu.CV.ML.MlInvoke.CvDTreeTrain(System.IntPtr,System.IntPtr,Emgu.CV.ML.MlEnum.DATA_LAYOUT_TYPE,System.IntPtr,System.IntPtr,System.IntPtr,System.IntPtr,System.IntPtr,Emgu.CV.ML.Structure.MCvDTreeParams)]