trait Classifier extends Model
The Classifier trait provides a common framework for several classifiers.
A classifier is for bounded responses. When the number of distinct responses
cannot be bounded by some integer 'k', a predictor should be used.
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- abstract def classify(z: VectoD): (Int, String, Double)
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
- z
the real vector to classify
- abstract def classify(z: VectoI): (Int, String, Double)
Given a new discrete data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
Given a new discrete data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability.
- z
the integer vector to classify
- abstract def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]
Test the accuracy of the classified results by cross-validation, returning the accuracy.
Test the accuracy of the classified results by cross-validation, returning the accuracy. The "test data" starts at 'testStart' and ends at 'testEnd', the rest of the data is "training data'.
- nx
the number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- abstract def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.
Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.
- nx
number of crosses and cross-validations (defaults to 10x).
- show
the show flag (show result from each iteration)
- abstract def eval(x_e: MatriD, y_e: VectoD): Model
Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model).
Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.
- x_e
the test/full data/input matrix (impl. classes should default to x)
- y_e
the test/full response/output vector (impl. classes should default to y)
- Definition Classes
- Model
- abstract def hparameter: HyperParameter
Return the model hyper-parameters (if none, return null).
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.
- Definition Classes
- Model
- abstract def parameter: VectoD
Return the vector of model parameter/coefficient values.
Return the vector of model parameter/coefficient values.
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- Model
- abstract def report: String
Return a basic report on the trained model.
Return a basic report on the trained model.
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- Model
- See also
'summary' method for more details
- abstract def reset(): Unit
Reset variables such as frequency counters.
- abstract def size: Int
Return the number of data vectors/points in the entire dataset (training + testing).
- abstract def test(itest: VectorI): Double
Test the quality of the training with a test dataset and return the fraction of correct classifications.
Test the quality of the training with a test dataset and return the fraction of correct classifications.
- itest
the indices of the instances considered test data
- abstract def train(itest: VectorI): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. The indices for the testing dataset are given and the training dataset consists of all the other instances. Must be implemented in any extending class.
- itest
the indices of the instances considered as testing data
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- val modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
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- def modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
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- def setStream(str: Int = 0): Unit
Set the random number 'stream' to 'str'.
Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.
- str
the new fixed random number stream
- val stream: Int
the random number stream {0, 1, ..., 999} to be used
the random number stream {0, 1, ..., 999} to be used
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- def test(testStart: Int, testEnd: Int): Double
Test the quality of the training with a test dataset and return the fraction of correct classifications.
Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.
- testStart
the beginning of test region (inclusive).
- testEnd
the end of test region (exclusive).
- def toString(): String
- Definition Classes
- AnyRef → Any
- def train(xx: MatriD = null, yy: VectoD = null): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.
- xx
the data/input matrix (impl. classes should ignore or default xx to x)
- yy
the response/classification vector (impl. classes should ignore or default yy to y)
- Definition Classes
- Classifier → Model
- def train(testStart: Int, testEnd: Int): Classifier
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.
Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.
- testStart
starting index of test region (inclusive) used in cross-validation
- testEnd
ending index of test region (exclusive) used in cross-validation
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