Packages

class DecisionTreeID3 extends ClassifierInt

The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered '0, ..., k-1'. Each column in the matrix represents a feature (e.g., Humidity). The 'vc' array gives the number of distinct values per feature (e.g., 2 for Humidity).

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ClassifierInt, Error, Classifier, AnyRef, Any
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  1. DecisionTreeID3
  2. ClassifierInt
  3. Error
  4. Classifier
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Instance Constructors

  1. new DecisionTreeID3(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: Array[Int] = null)

    x

    the data vectors stored as rows of a matrix

    y

    the class array, where y_i = class for row i of the matrix x

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    vc

    the value count array indicating number of distinct values per feature

Type Members

  1. case class FeatureNode(f: Int, branches: HashMap[Int, Node]) extends Node with Product with Serializable
  2. case class LeafNode(y: Int) extends Node with Product with Serializable
  3. abstract class Node extends AnyRef
  4. type Path = List[(Int, Int)]

Value Members

  1. def buildTree(path: Path): Node

    Extend the tree given a path e.g.

    Extend the tree given a path e.g. ((outlook, sunny), ...).

    path

    an existing path in the tree ((feature, value), ...)

  2. def calcCorrelation: MatriD

    Calculate the correlation matrix for the feature vectors 'fea'.

    Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.

    Definition Classes
    ClassifierInt
  3. def calcCorrelation2(zrg: Range, xrg: Range): MatriD

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.

    zrg

    the range of Z-columns

    xrg

    the range of X-columns

    Definition Classes
    ClassifierInt
  4. def classify(z: VectoI): (Int, String, Double)

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf.

    Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf. Return the best class, its name and FIX.

    z

    the data vector to classify

    Definition Classes
    DecisionTreeID3 → Classifier
  5. def classify(xx: MatriI): VectoI

    Classify all of the row vectors in matrix 'xx'.

    Classify all of the row vectors in matrix 'xx'.

    xx

    the row vectors to classify

    Definition Classes
    ClassifierInt
  6. 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. Override in classes that require precise real values for classification.

    z

    the real vector to classify

    Definition Classes
    ClassifierInt → Classifier
  7. def crossValidate(nx: Int = 10, show: Boolean = false): Double

    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'. FIX - should return a StatVector

    nx

    the number of crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  8. def crossValidateRand(nx: Int = 10, show: Boolean = false): Double

    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. This version of cross-validation relies on "subtracting" frequencies from the previously stored global data to achieve efficiency. FIX - are the comments correct? FIX - should return a StatVector

    nx

    number of crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    Classifier
  9. def dataset(f: Int, path: Path): Array[(Int, Int)]

    Extract column from matrix, filtering out values rows that are not on path.

    Extract column from matrix, filtering out values rows that are not on path.

    f

    the feature to consider (e.g., 2 (Humidity))

  10. def featureSelection(TOL: Double = 0.01): Unit

    Perform feature selection on the classifier.

    Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.

    TOL

    tolerance indicating negligible accuracy loss when removing features

    Definition Classes
    ClassifierInt
  11. def fit(y: VectoI, yp: VectoI, k: Int = 2): VectoD

    Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'.

    Return the quality of fit including 'acc', 'prec', 'recall', 'kappa'. Override to add more quality of fit measures.

    y

    the actual class labels

    yp

    the precicted class labels

    k

    the number of class labels

    Definition Classes
    Classifier
    See also

    ConfusionMat

    medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b

  12. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Classifier
  13. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  14. def frequency(dset: Array[(Int, Int)], value: Int): (Double, VectoD)

    Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of occurrence of the value for each classification (e.g., 0 (no), 1 (yes)) to estimate k probabilities.

    Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of occurrence of the value for each classification (e.g., 0 (no), 1 (yes)) to estimate k probabilities. Also, determine the fraction of training cases where the feature has this value (e.g., fraction where Humidity is High = 7/14).

    dset

    the list of data set tuples to consider (e.g. value, row index)

    value

    one of the possible values for this feature (e.g., 1 (High))

  15. def gain(f: Int, path: Path): Double

    Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.g., how well does Humidity with its values Normal and High indicate whether one will play tennis).

    Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.g., how well does Humidity with its values Normal and High indicate whether one will play tennis).

    f

    the feature to consider (e.g., 2 (Humidity))

  16. def mode(a: Array[Int]): Int

    Find the most frequent classification.

    Find the most frequent classification.

    a

    array of discrete classifications

  17. def reset(): Unit

    Reset or re-initialize the frequency tables and the probability tables.

    Reset or re-initialize the frequency tables and the probability tables.

    Definition Classes
    DecisionTreeID3 → Classifier
  18. def shiftToZero(): Unit

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Shift the 'x' Matrix so that the minimum value for each column equals zero.

    Definition Classes
    ClassifierInt
  19. def size: Int

    Return the number of data vectors/points in the entire dataset (training + testing),

    Return the number of data vectors/points in the entire dataset (training + testing),

    Definition Classes
    ClassifierInt → Classifier
  20. def test(xx: MatriI, yy: VectoI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    xx

    the integer-valued test vectors stored as rows of a matrix

    yy

    the test classification vector, where 'yy_i = class' for row 'i' of 'xx'

    Definition Classes
    ClassifierInt
  21. def test(itest: IndexedSeq[Int]): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    itest

    indices of the instances considered test data

    Definition Classes
    ClassifierInt → Classifier
  22. 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).

    Definition Classes
    Classifier
  23. def train(itest: IndexedSeq[Int]): DecisionTreeID3

    Train the decision tree.

    Train the decision tree.

    itest

    the indices of the instances considered as testing data@param testStart starting index of test region (inclusive) used in cross-validation.

    Definition Classes
    DecisionTreeID3 → Classifier
  24. def train(): 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.

    Definition Classes
    Classifier
  25. 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

    Definition Classes
    Classifier
  26. def vc_default: Array[Int]

    Return default values for binary input data (value count 'vc' set to 2).

    Return default values for binary input data (value count 'vc' set to 2).

    Definition Classes
    ClassifierInt
  27. def vc_fromData: Array[Int]

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    Definition Classes
    ClassifierInt
  28. def vc_fromData2(rg: Range): Array[Int]

    Return value counts calculated from the input data.

    Return value counts calculated from the input data. May wish to call 'shiftToZero' before calling this method.

    rg

    the range of columns to be considered

    Definition Classes
    ClassifierInt