@spec calcError(t(), Evision.ML.TrainData.t(), boolean()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments

• self: Evision.ML.DTrees.t()

• data: Evision.ML.TrainData.t().

  the training data

• test: bool.

  if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return

• retval: float

• resp: Evision.Mat.t().

  the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference only):

calcError(data, test[, resp]) -> retval, resp

@spec calcError(
  t(),
  Evision.ML.TrainData.t(),
  boolean(),
  [{atom(), term()}, ...] | nil
) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments

• self: Evision.ML.DTrees.t()

• data: Evision.ML.TrainData.t().

  the training data

• test: bool.

  if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return

• retval: float

• resp: Evision.Mat.t().

  the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference only):

calcError(data, test[, resp]) -> retval, resp

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.ML.DTrees.t()

Python prototype (for reference only):

clear() -> None

@spec create() :: t() | {:error, String.t()}

Creates the empty model

Return

• retval: Evision.ML.DTrees.t()

The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load\<DTrees>(filename).

Python prototype (for reference only):

create() -> retval

@spec empty(t()) :: boolean() | {:error, String.t()}

empty

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getCVFolds(t()) :: integer() | {:error, String.t()}

getCVFolds

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: int

@see setCVFolds/2

Python prototype (for reference only):

getCVFolds() -> retval

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

@spec getMaxCategories(t()) :: integer() | {:error, String.t()}

getMaxCategories

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: int

@see setMaxCategories/2

Python prototype (for reference only):

getMaxCategories() -> retval

@spec getMaxDepth(t()) :: integer() | {:error, String.t()}

getMaxDepth

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: int

@see setMaxDepth/2

Python prototype (for reference only):

getMaxDepth() -> retval

@spec getMinSampleCount(t()) :: integer() | {:error, String.t()}

getMinSampleCount

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: int

@see setMinSampleCount/2

Python prototype (for reference only):

getMinSampleCount() -> retval

@spec getPriors(t()) :: Evision.Mat.t() | {:error, String.t()}

getPriors

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: Evision.Mat.t()

@see setPriors/2

Python prototype (for reference only):

getPriors() -> retval

@spec getRegressionAccuracy(t()) :: number() | {:error, String.t()}

getRegressionAccuracy

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: float

@see setRegressionAccuracy/2

Python prototype (for reference only):

getRegressionAccuracy() -> retval

@spec getTruncatePrunedTree(t()) :: boolean() | {:error, String.t()}

getTruncatePrunedTree

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

@see setTruncatePrunedTree/2

Python prototype (for reference only):

getTruncatePrunedTree() -> retval

@spec getUse1SERule(t()) :: boolean() | {:error, String.t()}

getUse1SERule

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

@see setUse1SERule/2

Python prototype (for reference only):

getUse1SERule() -> retval

@spec getUseSurrogates(t()) :: boolean() | {:error, String.t()}

getUseSurrogates

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

@see setUseSurrogates/2

Python prototype (for reference only):

getUseSurrogates() -> retval

@spec getVarCount(t()) :: integer() | {:error, String.t()}

Returns the number of variables in training samples

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: int

Python prototype (for reference only):

getVarCount() -> retval

@spec isClassifier(t()) :: boolean() | {:error, String.t()}

Returns true if the model is classifier

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

Python prototype (for reference only):

isClassifier() -> retval

@spec isTrained(t()) :: boolean() | {:error, String.t()}

Returns true if the model is trained

Positional Arguments

• self: Evision.ML.DTrees.t()

Return

• retval: bool

Python prototype (for reference only):

isTrained() -> retval

@spec load(binary()) :: t() | {:error, String.t()}

Loads and creates a serialized DTrees from a file

Positional Arguments

• filepath: String.

  path to serialized DTree

Keyword Arguments

• nodeName: String.

  name of node containing the classifier

Return

• retval: Evision.ML.DTrees.t()

Use DTree::save to serialize and store an DTree to disk. Load the DTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Python prototype (for reference only):

load(filepath[, nodeName]) -> retval

@spec load(binary(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Loads and creates a serialized DTrees from a file

Positional Arguments

• filepath: String.

  path to serialized DTree

Keyword Arguments

• nodeName: String.

  name of node containing the classifier

Return

• retval: Evision.ML.DTrees.t()

Use DTree::save to serialize and store an DTree to disk. Load the DTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Python prototype (for reference only):

load(filepath[, nodeName]) -> retval

@spec predict(t(), Evision.Mat.maybe_mat_in()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments

• self: Evision.ML.DTrees.t()

• samples: Evision.Mat.t().

  The input samples, floating-point matrix

Keyword Arguments

• flags: int.

  The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return

• retval: float

• results: Evision.Mat.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@spec predict(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments

• self: Evision.ML.DTrees.t()

• samples: Evision.Mat.t().

  The input samples, floating-point matrix

Keyword Arguments

• flags: int.

  The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return

• retval: float

• results: Evision.Mat.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.ML.DTrees.t()
• fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.ML.DTrees.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

@spec setCVFolds(t(), integer()) :: t() | {:error, String.t()}

setCVFolds

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: int

@see getCVFolds/1

Python prototype (for reference only):

setCVFolds(val) -> None

@spec setMaxCategories(t(), integer()) :: t() | {:error, String.t()}

setMaxCategories

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: int

@see getMaxCategories/1

Python prototype (for reference only):

setMaxCategories(val) -> None

@spec setMaxDepth(t(), integer()) :: t() | {:error, String.t()}

setMaxDepth

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: int

@see getMaxDepth/1

Python prototype (for reference only):

setMaxDepth(val) -> None

@spec setMinSampleCount(t(), integer()) :: t() | {:error, String.t()}

setMinSampleCount

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: int

@see getMinSampleCount/1

Python prototype (for reference only):

setMinSampleCount(val) -> None

@spec setPriors(t(), Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}

setPriors

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: Evision.Mat.t()

@see getPriors/1

Python prototype (for reference only):

setPriors(val) -> None

@spec setRegressionAccuracy(t(), number()) :: t() | {:error, String.t()}

setRegressionAccuracy

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: float

@see getRegressionAccuracy/1

Python prototype (for reference only):

setRegressionAccuracy(val) -> None

@spec setTruncatePrunedTree(t(), boolean()) :: t() | {:error, String.t()}

setTruncatePrunedTree

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: bool

@see getTruncatePrunedTree/1

Python prototype (for reference only):

setTruncatePrunedTree(val) -> None

@spec setUse1SERule(t(), boolean()) :: t() | {:error, String.t()}

setUse1SERule

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: bool

@see getUse1SERule/1

Python prototype (for reference only):

setUse1SERule(val) -> None

@spec setUseSurrogates(t(), boolean()) :: t() | {:error, String.t()}

setUseSurrogates

Positional Arguments

• self: Evision.ML.DTrees.t()
• val: bool

@see getUseSurrogates/1

Python prototype (for reference only):

setUseSurrogates(val) -> None

@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.DTrees.t()

• trainData: Evision.ML.TrainData.t().

  training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments

• flags: int.

  optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return

• retval: bool

Python prototype (for reference only):

train(trainData[, flags]) -> retval

@spec train(t(), Evision.ML.TrainData.t(), [{atom(), term()}, ...] | nil) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.DTrees.t()

• trainData: Evision.ML.TrainData.t().

  training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments

• flags: int.

  optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return

• retval: bool

Python prototype (for reference only):

train(trainData[, flags]) -> retval

@spec train(t(), Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in()) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.DTrees.t()

• samples: Evision.Mat.t().

  training samples

• layout: int.

  See ml::SampleTypes.

• responses: Evision.Mat.t().

  vector of responses associated with the training samples.

Return

• retval: bool

Python prototype (for reference only):

train(samples, layout, responses) -> retval

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.ML.DTrees.t()
• fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.ML.DTrees.t()
• fs: Evision.FileStorage.t()
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None