class DecisionTreeC45 extends ClassifierReal with DecisionTree
The DecisionTreeC45 class implements a Decision Tree classifier using the
C4.5 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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At node for feature 'x_f', create children for possible discrete values of 'x_f'
(For continuous, pick a threshold to split into lower and higher values). Upon
splitting, some matrices need to be created for which 'x_f' column is removed and
each child only contains rows for its given value of 'x_f'.
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Instance Constructors
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new
DecisionTreeC45(x: MatriD, y: VectoI, isCont: Array[Boolean], fn_: Strings = null, k: Int = 2, cn_: Strings = null, vc: Array[Int] = null, td: Int = 0)
- 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
- isCont
Booleanvalue to indicate whether according feature is continuous- 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
- td
the maximum tree depth allowed (defaults to 0 => n, -1 => no depth constrint)
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
buildTree(dset: (MatriD, VectoI), path: List[(Int, Int)], depth: Int): Node
Recursively build the decision tree given a subset of data.
Recursively build the decision tree given a subset of data.
- dset
the dataset to build the subtree
- path
an existing path in the tree ((feature, value), ...)
- depth
the depth of the subtree being built
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def
calThreshold(f: Int, dset: (MatriD, VectoI)): Unit
Given a continuous feature, adjust its threshold to improve gain.
Given a continuous feature, adjust its threshold to improve gain.
- f
the feature index to consider
- dset
the dataset to consider
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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
- ClassifierReal
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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
- ClassifierReal
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def
classify(z: VectoD): (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 (some continuous features)
- Definition Classes
- DecisionTreeC45 → Classifier
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def
classify(xx: MatriD): 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
- ClassifierReal
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def
classify(z: VectoI): (Int, String, Double)
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles.
Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability
- z
the vector to classify
- Definition Classes
- ClassifierReal → Classifier
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def
clone(): AnyRef
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var
cn: Strings
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- protected
- Definition Classes
- ClassifierReal
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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
-
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
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def
dataset(f: Int, value: Int, xx: MatriD, yy: VectoI): (MatriD, VectoI)
Return a new 'x' matrix and 'y' vector for next step of constructing decision tree based upon values of the given feature 'f'.
Return a new 'x' matrix and 'y' vector for next step of constructing decision tree based upon values of the given feature 'f'. The rows are selected based on the threshold values for continuous features and discrete values otherwise.
- f
the feature index
- value
one of the feature values or 0 (<=) / 1 (> threshold) for a continuous feature
- xx
the data matrix containing feature/column f
- yy
the corresponding response/classification vector
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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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
- ClassifierReal
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def
finalize(): Unit
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- protected[java.lang]
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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 predicted class labels
- k
the number of class labels
- Definition Classes
- Classifier
- See also
ConfusionMatmedium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-part-i-b085d432082b
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def
fitLabel: Seq[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
- Definition Classes
- Classifier
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def
fitMap(y: VectoI, yp: VectoI, k: Int = 2): Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- y
the actual class labels
- yp
the predicted class labels
- k
the number of class labels
- Definition Classes
- Classifier
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
var
fn: Strings
- Attributes
- protected
- Definition Classes
- ClassifierReal
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def
frequency(dset: (MatriD, VectoI), f: Int, value: Double): (Double, VectoI, VectorD)
Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of occurrence 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 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 possibly restricted dataset to consider
- f
the feature column to consider (e.g., Humidity)
- value
one of the possible values for this feature (e.g., 1 (High))
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val
fset: Array[Boolean]
the set of features to turn on or off.
the set of features to turn on or off. All features are on by default. Used for feature selection.
- Attributes
- protected
- Definition Classes
- ClassifierReal
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def
gain(f: Int, dset: (MatriD, VectoI)): (Double, VectoI)
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))
- dset
the possibly restricted dataset to consider
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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val
m: Int
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
def
mode(y: Array[Int]): Int
Find the most frequent classification.
Find the most frequent classification.
- y
the array of discrete classifications
- Definition Classes
- DecisionTree
-
def
multivalued(x: MatriD): Boolean
Determine whether the matrix 'x' is multivalued (>= 2 distinct rows).
Determine whether the matrix 'x' is multivalued (>= 2 distinct rows).
- x
the given vector
- Definition Classes
- DecisionTree
-
val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierReal
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
printTree(vc: Array[Int]): Unit
Print the decision tree using 'prinT' method from
Nodeclass.Print the decision tree using 'prinT' method from
Nodeclass.- vc
the value count array (number of values for each feature)
- Definition Classes
- DecisionTree
-
def
reset(): Unit
Reset or re-initialize counters, if needed.
Reset or re-initialize counters, if needed.
- Definition Classes
- DecisionTree
-
def
size: Int
Return the number of data vectors in training/test-set (# rows).
Return the number of data vectors in training/test-set (# rows).
- Definition Classes
- ClassifierReal → Classifier
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(xx: MatriD, 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 real-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
- ClassifierReal
-
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
- ClassifierReal → Classifier
-
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
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
train(itest: IndexedSeq[Int]): DecisionTreeC45
Train the decision tree.
Train the decision tree.
- itest
the indices for the test data
- Definition Classes
- DecisionTreeC45 → Classifier
-
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
-
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
-
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). Also may be used for binning into two categories.
- Definition Classes
- ClassifierReal
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
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- @throws( ... )
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final
def
wait(arg0: Long, arg1: Int): Unit
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- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
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- @native() @throws( ... )
- val x: MatriD
- val y: VectoI