Packages

package classifier

The analytics package contains classes, traits and objects for analytics focused on classification.

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  1. abstract class BayesClassifier extends ClassifierInt with BayesMetrics

    The BayesClassifier object provides factory methods for building Bayesian classifiers.

    The BayesClassifier object provides factory methods for building Bayesian classifiers. The following types of classifiers are currently supported: NaiveBayes - Naive Bayes classifier SelNaiveBayes - Selective Naive Bayes classifier OneBAN - Augmented Naive Bayes (1-BAN) classifier SelOneBAN - Augmented Selective Naive Bayes (Selective 1-BAN) classifier TANBayes - Tree Augmented Naive Bayes classifier SelTANBayes - Selective Tree Augmented Naive Bayes classifier TwoBAN_OS - Ordering-based Bayesian Network (2-BAN with Order Swapping) -----------------------------------------------------------------------------

  2. class BayesClfML extends Classifier

    The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data.

    The BayesClfML class implements an Integer-Based Naive Bayes Multi-Label Classifier, which is a commonly used such classifier for discrete input data. The classifier is trained using a data matrix 'x' and a classification matrix 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered 0, ..., k-1. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities.

    See also

    www.aia-i.com/ijai/sample/vol3/no2/173-188.pdf -----------------------------------------------------------------------------

  3. trait BayesMetrics extends AnyRef

    The BayesMetrics trait provides scoring methods.

  4. class BayesNetwork extends BayesClassifier

    The BayesNetwork class implements a Bayesian Network Classifier.

    The BayesNetwork class implements a Bayesian Network Classifier. It classifies a data vector 'z' by determining which of 'k' classes has the highest Joint Probability of 'z' and the outcome (i.e., one of the 'k' classes) of occurring. The Joint Probability calculation is factored into multiple calculations of Conditional Probability. Conditional dependencies are specified using a Directed Acyclic Graph 'DAG'. Nodes are conditionally dependent on their parents only. Conditional probability are recorded in tables. Training is achieved by ... -----------------------------------------------------------------------------

  5. trait Classifier extends AnyRef

    The Classifier trait provides a common framework for several classifiers.

    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.

  6. abstract class ClassifierInt extends Classifier with Error

    The ClassifierInt abstract class provides a common foundation for several classifiers that operate on integer-valued data.

  7. abstract class ClassifierReal extends Classifier with Error

    The ClassifierReal abstract class provides a common foundation for several classifiers that operate on real-valued data.

  8. class DAG extends AnyRef

    The 'DAG' class provides a data structure for storing directed acyclic graphs.

  9. class DecisionTreeC45 extends ClassifierInt

    The DecisionTreeC45 class implements a Decision Tree classifier using the C4.5 algorithm.

    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).

  10. class DecisionTreeID3 extends ClassifierInt

    The DecisionTreeID3 class implements a Decision Tree classifier using the ID3 algorithm.

    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).

  11. class DynBayesNetwork extends Classifier

    The DynBayesNetwork class provides Dynamic Bayesian Network (DBN) models.

  12. class GMM extends Classifier

    The GMM class is used for univariate Gaussian Mixture Models.

    The GMM class is used for univariate Gaussian Mixture Models. Given a sample, thought to be generated according to 'k' Normal distributions, estimate the values for the 'mu' and 'sig2' parameters for the Normal distributions. Given a new value, determine which class (0, ..., k-1) it is most likely to have come from. FIX: need a class for multivariate Gaussian Mixture Models. FIX: need to adapt for clustering. -----------------------------------------------------------------------------

  13. class HiddenMarkov extends Classifier

    The HiddenMarkov classes provides Hidden Markov Models (HMM).

    The HiddenMarkov classes provides Hidden Markov Models (HMM). An HMM model consists of a probability vector 'pi' and probability matrices 'a' and 'b'. The discrete-time system is characterized by a hidden 'state(t)' and an 'observed(t)' symbol at time 't'.

    pi(j) = P(state(t) = j) a(i, j) = P(state(t+1) = j | state(t) = i) b(i, k) = P(observed(t) = k | state(t) = i)

    model (pi, a, b)

    See also

    www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf

  14. class KNN_Classifier extends ClassifierReal

    The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes.

    The KNN_Classifier class is used to classify a new vector 'z' into one of 'k' classes. It works by finding its 'knn' nearest neighbors. These neighbors essentially vote according to their classification. The class with most votes is selected as the classification of 'z'. Using a distance metric, the 'knn' vectors nearest to 'z' are found in the training data, which is stored row-wise in the data matrix 'x'. The corresponding classifications are given in the vector 'y', such that the classification for vector 'x(i)' is given by 'y(i)'.

  15. class LDA extends ClassifierReal

    The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.

    The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.

    See also

    en.wikipedia.org/wiki/Linear_discriminant_analysis

  16. class LogisticRegression extends ClassifierReal

    The LogisticRegression class supports (binomial) logistic regression.

    The LogisticRegression class supports (binomial) logistic regression. In this case, 'x' may be multi-dimensional '[1, x_1, ... x_k]'. Fit the parameter vector 'b' in the logistic regression equation

    y = b dot x + e = b_0 + b_1 * x_1 + ... b_k * x_k + e

    where 'e' represents the residuals (the part not explained by the model) and 'y' is now binary.

    See also

    see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

  17. class NaiveBayes extends NaiveBayes0

    The same classifier but uses an optimized cross-validation technique.

    The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------

  18. class NaiveBayes0 extends BayesClassifier

    The NaiveBayes0 class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The NaiveBayes0 class implements an Integer-Based Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities.

    This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------

  19. class NaiveBayesR extends ClassifierReal

    The NaiveBayesR class implements a Gaussian Naive Bayes Classifier, which is the most commonly used such classifier for continuous input data.

    The NaiveBayesR class implements a Gaussian Naive Bayes Classifier, which is the most commonly used such classifier for continuous input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional density functions based on the Normal (Gaussian) distribution. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional densities. -----------------------------------------------------------------------------

  20. class OneBAN extends OneBAN0

    The same classifier but uses an optimized cross-validation technique.

    The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------

  21. class OneBAN0 extends BayesClassifier

    The OneBAN class implements an Integer-Based One-parent BN Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The OneBAN class implements an Integer-Based One-parent BN Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables.

    This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------

  22. class PGMHD3 extends BayesClassifier

    The PGMHD3 class implements a three level Bayes Classifier for discrete input data.

    The PGMHD3 class implements a three level Bayes Classifier for discrete input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------

  23. class PGMHD3cp extends BayesClassifier

    The PGMHD3cp class implements a three level Bayes Classifier for discrete input data.

    The PGMHD3cp class implements a three level Bayes Classifier for discrete input data. 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. Prior probabilities are calculated based on the frequency/population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier is naive, because it assumes feature independence and therefore simply multiplies the conditional probabilities. ----------------------------------------------------------------------------- [ x ] -> [ x z ] where x features are level 2 and z features are level 3. -----------------------------------------------------------------------------

  24. class PGMHD3fl extends BayesClassifier

    The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels.

    The PGMHD3fl class implements a three-level Probabilistic Classifier for discrete (binary) input data, based on flow from bottom to top levels. The classifier is trained using the following data matrices:

    'x' - the mid/features 'X' (level 1) 'z' - the low/feature 'Z' (level 2) 'y' - the top/class 'C' (level 0)

    Each random variable 'C_j, X_k and Z_l' is binary ('vc = 2', 'k = 2'), where '1' indicates occurrence, while '0' indicates no evidence of occurreence. Frequency counts and classification scores are computed from a training-set. -----------------------------------------------------------------------------

  25. class SupportVectorMachine extends ClassifierReal

    The SupportVectorMachine class is a translation of Pseudo-Code from a modified SMO (Modification 2) found at the above URL's into Scala and includes a few simplifications (e.g., currently only works for linear kernels, dense data and binary classification).

  26. class TANBayes extends TANBayes0

    The same classifier but uses an optimized cross-validation technique.

    The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------

  27. class TANBayes0 extends BayesClassifier

    The TANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data.

    The TANBayes0 class implements an Integer-Based Tree Augmented Naive Bayes Classifier, which is a commonly used such classifier for discrete input data. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables.

    This classifier uses the standard cross-validation technique. -----------------------------------------------------------------------------

  28. class TabuFeatures extends AnyRef

    The TabuFeatures keeps track of pairs of features, so they are not repeatedly tried.

  29. class TwoBAN_OS extends TwoBAN_OS0

    The same classifier but uses an optimized cross-validation technique ------------------------------------------------------------------------------

  30. class TwoBAN_OS0 extends BayesClassifier

    The TwoBAN_OS0 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data.

    The TwoBAN_OS0 class implements an Integer-Based Bayesian Network Classifier, which is a commonly used such classifier for discrete input data. Each node is limited to have at most 2 parents, and hence the "2" in the class name TwoBAN_OS0. 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. Prior probabilities are calculated based on the population of each class in the training-set. Relative posterior probabilities are computed by multiplying these by values computed using conditional probabilities. The classifier supports limited dependency between features/variables.

    This classifier uses the standard cross-validation technique. ------------------------------------------------------------------------------

Value Members

  1. val BASE_DIR: String

    The relative path for base directory

  2. object BayesClassifier

    The BayesClassifier object provides factory methods for building Bayes classifiers.

  3. object BayesClassifierTest extends App

    The BayesClassifierTest object is used to test the BayesClassifier class.

    The BayesClassifierTest object is used to test the BayesClassifier class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.BayesClassifierTest

  4. object BayesClassifierTest10 extends App

    The BayesClassifierTest10 object is used to test the BayesClassifier class.

    The BayesClassifierTest10 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest10

  5. object BayesClassifierTest11 extends App

    The BayesClassifierTest11 object is used to test the BayesClassifier class.

    The BayesClassifierTest11 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest11

  6. object BayesClassifierTest2 extends App

    The BayesClassifierTest2 object is used to test the BayesClassifier class.

    The BayesClassifierTest2 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest2

  7. object BayesClassifierTest3 extends App

    The BayesClassifierTest3 object is used to test the BayesClassifier class.

    The BayesClassifierTest3 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest3

  8. object BayesClassifierTest4 extends App

    The BayesClassifierTest4 object is used to test the BayesClassifier class.

    The BayesClassifierTest4 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest4

  9. object BayesClassifierTest5 extends App

    The BayesClassifierTest5 object is used to test the BayesClassifier class.

    The BayesClassifierTest5 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest5

  10. object BayesClassifierTest6 extends App

    The BayesClassifierTest6 object is used to test the BayesClassifier class.

    The BayesClassifierTest6 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest6

  11. object BayesClassifierTest7 extends App

    The BayesClassifierTest7 object is used to test the BayesClassifier class.

    The BayesClassifierTest7 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest7

  12. object BayesClassifierTest8 extends App

    The BayesClassifierTest8 object is used to test the BayesClassifier class.

    The BayesClassifierTest8 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest8

  13. object BayesClassifierTest9 extends App

    The BayesClassifierTest9 object is used to test the BayesClassifier class.

    The BayesClassifierTest9 object is used to test the BayesClassifier class. > run-main scalation.analytics.classifier.BayesClassifierTest9

  14. object BayesClfMLTest extends App

    The BayesClfMLTest object is used to test the BayesClfML class.

    The BayesClfMLTest object is used to test the BayesClfML class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.BayesClfMLTest

  15. object BayesNetworkTest extends App

    The BayesNetworkTest object is used to test the BayesNetwork class.

    The BayesNetworkTest object is used to test the BayesNetwork class. Ex: Classify whether a person has a Back Ache.

    See also

    www.eng.tau.ac.il/~bengal/BN.pdf > run-main sclation.analytics.classifier.BayesNetworkTest

  16. object ClassifierInt

    The ClassifierInt companion object provides methods to read in data matrices in a combined 'xy' format that can be later decomposed into 'x' the feature data matrix and 'y' the classification vector.

  17. object DecisionTreeC45

    DecisionTreeC45 is the companion object for the DecisionTreeC45 class.

  18. object DecisionTreeC45Test extends App

    The DecisionTreeC45Test object is used to test the DecisionTreeC45 class.

    The DecisionTreeC45Test object is used to test the DecisionTreeC45 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

    See also

    http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm

  19. object DecisionTreeID3

    DecisionTreeID3 is the companion object for the DecisionTreeID3 class.

  20. object DecisionTreeID3Test extends App

    The DecisionTreeID3Test object is used to test the DecisionTreeID3 class.

    The DecisionTreeID3Test object is used to test the DecisionTreeID3 class. Ex: Classify (No/Yes) whether a person will play tennis based on the measured features.

    See also

    http://www.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/2.htm

  21. object DynBayesNetworkTest extends App

    The DynBayesNetworkTest object is used to test the DynBayesNetwork class.

    The DynBayesNetworkTest object is used to test the DynBayesNetwork class. > run-main scalation.analytics.classifier.DynBayesNetworkTest

  22. object GMMTest extends App

    The GMMTest object is used to test the GMM class.

    The GMMTest object is used to test the GMM class. > run-main scalation.analytics.classifier.GMMTest

  23. object HiddenMarkovTest extends App

    The HiddenMarkovTest object is used to test the HiddenMarkov class.

    The HiddenMarkovTest object is used to test the HiddenMarkov class. Given model '(pi, a, b)', determine the probability of the observations 'ob'.

    See also

    www.cs.sjsu.edu/~stamp/RUA/HMM.pdf (exercise 1). > run-main scalation.analytics.classifieu.HiddenMarkovTest

  24. object HiddenMarkovTest2 extends App

    The HiddenMarkovTest2 object is used to test the HiddenMarkov class.

    The HiddenMarkovTest2 object is used to test the HiddenMarkov class. Train the model (pi, a, b) based on the observed data. > run-main scalation.analytics.classifier.HiddenMarkovTest2

  25. object KNN_ClassifierTest extends App

    The KNN_ClassifierTest object is used to test the KNN_Classifier class.

  26. object LDATest extends App

    The LDATest is used to test the LDA class.

    The LDATest is used to test the LDA class.

    See also

    people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > run-main scalation.analytics.classifier.LDATest

  27. object LogisticRegressionTest extends App

    The LogisticRegressionTest object tests the LogisticRegression class.

    The LogisticRegressionTest object tests the LogisticRegression class.

    See also

    www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aci = 29.533, pseudo_rSq = 0.4178 > run-main scalation.analytics.classifier.LogisticRegressionTest

  28. object LogisticRegressionTest2 extends App

    The LogisticRegressionTest object tests the LogisticRegression class.

    The LogisticRegressionTest object tests the LogisticRegression class.

    See also

    www.stat.wisc.edu/~mchung/teaching/.../GLM.logistic.Rpackage.pdf > run-main scalation.analytics.classifier.classifier.LogisticRegressionTest2

    statmaster.sdu.dk/courses/st111/module03/index.html

  29. object NaiveBayes

    NaiveBayes is the companion object for the NaiveBayes class.

  30. object NaiveBayes0

    NaiveBayes0 is the companion object for the NaiveBayes0 class.

  31. object NaiveBayesMLTest2 extends App

    The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class.

    The NaiveBayesMLTest2 object is used to test the 'NaiveBayesML' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.NaiveBayesMLTest2

  32. object NaiveBayesMLTest3 extends App

    The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class.

    The NaiveBayesMLTest3 object is used to test the 'NaiveBayesML' class. > run-main scalation.analytics.classifier.NaiveBayesMLTest3

  33. object NaiveBayesRTest extends App

    The NaiveBayesRTest object is used to test the NaiveBayesR class.

    The NaiveBayesRTest object is used to test the NaiveBayesR class. * Ex: Classify whether a person is male (M) or female (F) based on the measured features.

    See also

    en.wikipedia.org/wiki/Naive_Bayes_classifier > run-main scalation.analytics.classifier.NaiveBayesRTest

  34. object NaiveBayesTest extends App

    The NaiveBayesTest object is used to test the NaiveBayes class.

    The NaiveBayesTest object is used to test the NaiveBayes class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.NaiveBayesTest

  35. object NaiveBayesTest2 extends App

    The NaiveBayesTest2 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest2 object is used to test the 'NaiveBayes' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.NaiveBayesTest2

  36. object NaiveBayesTest3 extends App

    The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.

    See also

    docs.roguewave.com/imsl/java/7.3/manual/api/com/imsl/datamining/NaiveBayesClassifierEx2.html > run-main scalation.analytics.classifier.NaiveBayesTest3

    archive.ics.uci.edu/ml/datasets/Lenses

  37. object NaiveBayesTest4 extends App

    The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest4 object is used to test the 'NaiveBayes' class. > run-main scalation.analytics.classifier.NaiveBayesTest4

  38. object NaiveBayesTest5 extends App

    The NaiveBayesTest5 object is used to test the 'NaiveBayes' class.

    The NaiveBayesTest5 object is used to test the 'NaiveBayes' class. > run-main scalation.analytics.classifier.NaiveBayesTest5

  39. object OneBAN

    OneBAN is the companion object for the OneBAN class.

  40. object OneBAN0

    OneBAN0 is the companion object for the OneBAN0 class.

  41. object OneBANTest extends App

    The OneBANTest object is used to test the OneBAN class.

    The OneBANTest object is used to test the OneBAN class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-OneBAN-example.pdf > run-main scalation.analytics.classifier.OneBANTest

  42. object OneBANTest2 extends App

    The OneBANTest2 object is used to test the OneBAN class.

    The OneBANTest2 object is used to test the OneBAN class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.OneBANTest2

  43. object OneBANTest3 extends App

    The OneBANTest3 object is used to test the OneBAN class.

    The OneBANTest3 object is used to test the OneBAN class. > run-main scalation.analytics.classifier.OneBANTest3

  44. object PGMHD3

    PGMHD3 is the companion object for the PGMHD3 class.

  45. object PGMHD3Test extends App

    The PGMHD3Test object is used to test the PGMHD3 class.

    The PGMHD3Test object is used to test the PGMHD3 class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3Test

  46. object PGMHD3Test2 extends App

    The PGMHD3Test2 object is used to test the 'PGMHD3' class.

    The PGMHD3Test2 object is used to test the 'PGMHD3' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.PGMHD3Test2

  47. object PGMHD3Test3 extends App

    The PGMHD3Test3 object is used to test the 'PGMHD3' class.

    The PGMHD3Test3 object is used to test the 'PGMHD3' class. > run-main scalation.analytics.classifier.PGMHD3Test3

  48. object PGMHD3cp

    PGMHD3cp is the companion object for the PGMHD3cp class.

  49. object PGMHD3cpTest extends App

    The PGMHD3cpTest object is used to test the PGMHD3cp class.

    The PGMHD3cpTest object is used to test the PGMHD3cp class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3cpTest

  50. object PGMHD3cpTest2 extends App

    The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class.

    The PGMHD3cpTest2 object is used to test the 'PGMHD3cp' class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.PGMHD3cpTest2

  51. object PGMHD3cpTest3 extends App

    The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class.

    The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class. > run-main scalation.analytics.classifier.PGMHD3cpTest3

  52. object PGMHD3fl

    PGMHD3fl is the companion object for the PGMHD3fl class.

  53. object PGMHD3flTest extends App

    The PGMHD3flTest object is used to test the PGMHD3fl class.

    The PGMHD3flTest object is used to test the PGMHD3fl class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-naiveBayes-example.pdf > run-main scalation.analytics.classifier.PGMHD3flTest

  54. object SupportVectorMachineTest extends App

    The SupportVectorMachineTest is used to test the SupportVectorMachine class.

    The SupportVectorMachineTest is used to test the SupportVectorMachine class. > run-main scalation.analytics.classifier.SupportVectorMachineTest

  55. object SupportVectorMachineTest2 extends App

    The SupportVectorMachineTest2 is used to test the SupportVectorMachine class.

  56. object TANBayes

    The TANBayes object is the companion object for the TANBayes class.

  57. object TANBayes0

    The TANBayes0 object is the companion object for the TANBayes0 class.

  58. object TANBayesTest extends App

    The TANBayesTest object is used to test the TANBayes0 class.

    The TANBayesTest object is used to test the TANBayes0 class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-AugNaiveBayes-example.pdf > run-main scalation.analytics.classifier.TANBayesTest

  59. object TANBayesTest2 extends App

    The TANBayesTest2 object is used to test the TANBayes0 class.

    The TANBayesTest2 object is used to test the TANBayes0 class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.TANBayesTest2

  60. object TANBayesTest3 extends App

    The TANBayesTest3 object is used to test the TANBayes0 class.

    The TANBayesTest3 object is used to test the TANBayes0 class. > run-main scalation.analytics.classifier.TANBayesTest3

  61. object TwoBAN_OS

    The TwoBAN_OS object is the companion object for the TwoBAN_OS class.

  62. object TwoBAN_OS0

    The TwoBAN_OS0 object is the companion object for the TwoBAN_OS0 class.

  63. object TwoBAN_OSTest extends App

    The TwoBAN_OSTest object is used to test the TwoBAN_OS0 class.

    The TwoBAN_OSTest object is used to test the TwoBAN_OS0 class. Classify whether a car is more likely to be stolen (1) or not (1).

    See also

    www.inf.u-szeged.hu/~ormandi/ai2/06-TwoBAN_OS0-example.pdf > run-main scalation.analytics.classifier.TwoBAN_OSTest

  64. object TwoBAN_OSTest2 extends App

    The TwoBAN_OSTest2 object is used to test the TwoBAN_OS0 class.

    The TwoBAN_OSTest2 object is used to test the TwoBAN_OS0 class. Given whether a person is Fast and/or Strong, classify them as making C = 1 or not making C = 0 the football team. > run-main scalation.analytics.classifier.TwoBAN_OSTest2

  65. object TwoBAN_OSTest3 extends App

    The TwoBAN_OSTest3 object is used to test the TwoBAN_OS0 class.

    The TwoBAN_OSTest3 object is used to test the TwoBAN_OS0 class. > run-main scalation.analytics.classifier.TwoBAN_OSTest3

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