class DecisionTreeC45 extends ClassifierInt
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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Instance Constructors
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new
DecisionTreeC45(x: MatriI, y: VectoI, fn: Array[String], isCont: Array[Boolean], k: Int, cn: Array[String], vc: VectoI = 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
- isCont
Booleanvalue to indicate whether according feature is continuous- k
the number of classes
- cn
the names for all classes
- vc
the value count array indicating number of distinct values per feature
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(opt: (Int, Double)): Unit
Given the next most distinguishing feature/attribute, extend the decision tree.
Given the next most distinguishing feature/attribute, extend the decision tree.
- opt
the optimal feature and its gain
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def
calThreshold(f: Int): 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
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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
- ClassifierInt
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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
- ClassifierInt
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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, it ane and FIX.
- z
the data vector to classify (some continuous features)
- Definition Classes
- DecisionTreeC45 → ClassifierInt → Classifier
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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 its FIX.
- z
the data vector to classify (purely discrete features)
- Definition Classes
- DecisionTreeC45 → Classifier
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def
clone(): AnyRef
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- protected[java.lang]
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def
crossValidate(nx: Int = 10): 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'.
- nx
the number of crosses and cross-validations (defaults to 5x).
- Definition Classes
- Classifier
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def
crossValidateRand(nx: Int = 10): 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.
- nx
number of crosses and cross-validations (defaults to 10x).
- Definition Classes
- Classifier
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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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
- ClassifierInt
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def
finalize(): Unit
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final
def
flaw(method: String, message: String): Unit
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def
frequency(fCol: VectoI, value: Int, cont: Boolean = false, thres: Double = 0): (Double, 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).
- fCol
a feature column to consider (e.g., Humidity)
- value
one of the possible values for this feature (e.g., 1 (High))
- cont
indicates whether is calculating continuous feature
- thres
threshold for continuous feature
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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
- ClassifierInt
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def
gain(f: Int): 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))
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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/test-set (# rows)
the number of data vectors in training/test-set (# rows)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
md: Double
the training-set size as a Double
the training-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
n: Int
the number of features/variables (# columns)
the number of features/variables (# columns)
- Attributes
- protected
- Definition Classes
- ClassifierInt
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val
nd: Double
the feature-set size as a Double
the feature-set size as a Double
- Attributes
- protected
- Definition Classes
- ClassifierInt
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
nextXY(f: Int, value: Int): (MatrixI, VectorI)
Return new x matrix and y array for next step of constructing decision tree.
Return new x matrix and y array for next step of constructing decision tree.
- f
the feature index
- value
one of the features values
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final
def
notify(): Unit
- Definition Classes
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
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def
printTree(): Unit
Print out the decision tree using Breadth First Search (BFS).
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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
- DecisionTreeC45 → Classifier
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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
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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
- ClassifierInt → Classifier
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(itest: VectorI): 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
-
def
test(xx: MatrixI, yy: VectorI): 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
-
def
test(testStart: Int, testEnd: 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.
- testStart
beginning of test region (inclusive)
- testEnd
end of test region (exclusive)
- Definition Classes
- ClassifierInt → Classifier
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def
toString(): String
- Definition Classes
- AnyRef → Any
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def
train(testStart: Int, testEnd: Int): Unit
Train the classifier, i.e., determine which feature provides the most information gain and select it as the root of the decision tree.
Train the classifier, i.e., determine which feature provides the most information gain and select it as the root of the decision tree.
- testStart
starting index of test region (inclusive) used in cross-validation.
- testEnd
ending index of test region (exclusive) used in cross-validation.
- Definition Classes
- DecisionTreeC45 → Classifier
-
def
train(): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- Definition Classes
- Classifier
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def
train(itest: IndexedSeq[Int]): Unit
Given a set of data vectors and their classifications, build a classifier.
Given a set of data vectors and their classifications, build a classifier.
- itest
the indices of the instances considered as testing data
- Definition Classes
- Classifier
-
def
vc_default: VectorI
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
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def
vc_fromData: VectorI
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
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def
vc_fromData2(rg: Range): VectorI
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
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
- val x: MatriI
- val y: VectoI