classifier

package classifier

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

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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 OneBAN - Augmented Naive Bayes (1-BAN) classifier TANBayes - Tree Augmented Naive Bayes classifier TwoBAN_OS - Ordering-based Bayesian Network (2-BAN with Order Swapping) -----------------------------------------------------------------------------
trait BayesMetrics extends AnyRef
The BayesMetrics trait provides scoring methods.
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 ... -----------------------------------------------------------------------------
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.
abstract class ClassifierInt extends Classifier with Error
The ClassifierInt abstract class provides a common foundation for several classifiers that operate on integer-valued data.
abstract class ClassifierReal extends Classifier with Error
The ClassifierReal abstract class provides a common foundation for several classifiers that operate on real-valued data.
class ConfusionMat extends AnyRef
The ConfusionMat object provides functions for determining the confusion matrix as well as derived quality metrics such as accuracy, precsion, recall, and Cohen's kappa coefficient.
class DAG extends AnyRef
The 'DAG' class provides a data structure for storing directed acyclic graphs.
class DecisionTreeC45 extends ClassifierReal
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).
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).
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. -----------------------------------------------------------------------------
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
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 'kappa' 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 'kappa' 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)'.
class LDA extends ClassifierReal
The LDA class implements a Linear Discriminant Analysis 'LDA' classifier.
The LDA class implements a Linear Discriminant Analysis 'LDA' classifier. It places a vector into a group according to its maximal discriminant function. FIX - currently only works when the number of classes 'k' = 2.

See also
en.wikipedia.org/wiki/Linear_discriminant_analysis
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
logit (p_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
class NaiveBayes extends NaiveBayes0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
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. Class probabilities are calculated based on the frequency of each class in the training-set. Relative 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. -----------------------------------------------------------------------------
class NullModel extends ClassifierInt
The NullModel class implements an Integer-Based Null Model Classifier, which is a simple classifier for discrete input data.
The NullModel class implements an Integer-Based Null Model Classifier, which is a simple classifier for discrete input data. The classifier is trained just using a classification vector 'y'. Each data instance is classified into one of 'k' classes numbered 0, ..., k-1.
class OneBAN extends OneBAN0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
class RandomForest extends ClassifierReal
The RandomForest class uses randomness for building descision trees in classification.
The RandomForest class uses randomness for building descision trees in classification. It randomly selects sub-samples with 'size = bR * sample-size' from the sample (with replacement) and uses the 'fS' number of sub-features to build the trees, and to classify by voting from all of the trees.
class SimpleLDA extends ClassifierReal
The SimpleLDA class implements a Linear Discriminant Analysis 'LDA' classifier.
The SimpleLDA class implements a Linear Discriminant Analysis 'LDA' classifier. It places a value into a group according to its maximal discriminant function.

See also
en.wikipedia.org/wiki/Linear_discriminant_analysis
class SimpleLogisticRegression extends ClassifierReal
The SimpleLogisticRegression class supports (binomial) logistic regression.
The SimpleLogisticRegression class supports (binomial) logistic regression. In this case, 'x' is two-dimensional '[1, x_1]'. Fit the parameter vector 'b' in the logistic regression equation
logit (p_y) = b dot x + e = b_0 + b_1 * x_1 + 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
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).
class TANBayes extends TANBayes0
The same classifier but uses an optimized cross-validation technique.
The same classifier but uses an optimized cross-validation technique. -----------------------------------------------------------------------------
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. -----------------------------------------------------------------------------
class TabuFeatures extends AnyRef
The TabuFeatures keeps track of pairs of features, so they are not repeatedly tried.
class TwoBAN_OS extends TwoBAN_OS0
The same classifier but uses an optimized cross-validation technique ------------------------------------------------------------------------------
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

val BASE_DIR: String
The relative path for base directory
object BayesClassifier
The BayesClassifier object provides factory methods for building Bayes classifiers.
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 > runMain scalation.analytics.classifier.BayesClassifierTest
object BayesClassifierTest10 extends App
The BayesClassifierTest10 object is used to test the BayesClassifier class.
The BayesClassifierTest10 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest10
object BayesClassifierTest11 extends App
The BayesClassifierTest11 object is used to test the BayesClassifier class.
The BayesClassifierTest11 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest11
object BayesClassifierTest2 extends App
The BayesClassifierTest2 object is used to test the BayesClassifier class.
The BayesClassifierTest2 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest2
object BayesClassifierTest3 extends App
The BayesClassifierTest3 object is used to test the BayesClassifier class.
The BayesClassifierTest3 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest3
object BayesClassifierTest4 extends App
The BayesClassifierTest4 object is used to test the BayesClassifier class.
The BayesClassifierTest4 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest4
object BayesClassifierTest5 extends App
The BayesClassifierTest5 object is used to test the BayesClassifier class.
The BayesClassifierTest5 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest5
object BayesClassifierTest6 extends App
The BayesClassifierTest6 object is used to test the BayesClassifier class.
The BayesClassifierTest6 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest6
object BayesClassifierTest7 extends App
The BayesClassifierTest7 object is used to test the BayesClassifier class.
The BayesClassifierTest7 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest7
object BayesClassifierTest8 extends App
The BayesClassifierTest8 object is used to test the BayesClassifier class.
The BayesClassifierTest8 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest8
object BayesClassifierTest9 extends App
The BayesClassifierTest9 object is used to test the BayesClassifier class.
The BayesClassifierTest9 object is used to test the BayesClassifier class. > runMain scalation.analytics.classifier.BayesClassifierTest9
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 > runMain sclation.analytics.classifier.BayesNetworkTest
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.
object ConfusionMatTest extends App
The ConfusionMatTest object is used to test the ConfusionMatclass. > runMain scalation.analytics.classifier.ConfusionMatTest
object DecisionTreeC45
DecisionTreeC45 is the companion object for the DecisionTreeC45 class.
object DecisionTreeC45_Test 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 > runMain scalation.analytics.classifier.DecisionTreeC45_Test
object DecisionTreeC45_Test2 extends App
The DecisionTreeC45_Test2 object is used to test the DecisionTreeC45 class using the well-known winequality dataset.
The DecisionTreeC45_Test2 object is used to test the DecisionTreeC45 class using the well-known winequality dataset. > runMain scalation.analytics.classifier.DecisionTreeC45_Test2
object DecisionTreeID3
DecisionTreeID3 is the companion object for the DecisionTreeID3 class.
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
object ExampleIris
The ExampleIris object is used to test all classifiers.
The ExampleIris object is used to test all classifiers. This is the well-known classification problem on how to classify a flower
val x = xy.sliceCol (1, 5) // columns 1, 2, 3, 4 val y = xy.col (5).toInt // column 5

See also
https://en.wikipedia.org/wiki/Iris_flower_data_set
object ExampleIrisTest extends App
The ExampleIrisTest test several classifiers on the Iris dataset.
The ExampleIrisTest test several classifiers on the Iris dataset. > runMain scalation.analytics.classifier.ExampleIrisTest
object ExampleTennis
The ExampleTennis object is used to test all integer based classifiers.
The ExampleTennis object is used to test all integer based classifiers. This is the well-known classification problem on whether to play tennis based on given weather conditions. Applications may need to slice 'xy'.
val x = xy.sliceCol (0, 4) // columns 0, 1, 2, 3 val y = xy.col (4) // column 4

See also
euclid.nmu.edu/~mkowalcz/cs495f09/slides/lesson004.pdf
object ExampleTennisTest extends App
The ExampleTennisTest test several classifiers on the Tennis dataset.
The ExampleTennisTest test several classifiers on the Tennis dataset. > runMain scalation.analytics.classifier.ExampleTennisTest
object GMMTest extends App
The GMMTest object is used to test the GMM class.
The GMMTest object is used to test the GMM class. > runMain scalation.analytics.classifier.GMMTest
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). > runMain scalation.analytics.classifieu.HiddenMarkovTest
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. > runMain scalation.analytics.classifier.HiddenMarkovTest2
object KNN_Classifier
The KNN_Classifier companion object provides a factory method.
object KNN_ClassifierTest extends App
The KNN_ClassifierTest object is used to test the KNN_Classifier class.
The KNN_ClassifierTest object is used to test the KNN_Classifier class. > runMain scalation.analytics.classifier.KNN_ClassifierTest
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 > runMain scalation.analytics.classifier.LDATest
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 > runMain scalation.analytics.classifier.LogisticRegressionTest
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 > runMain scalation.analytics.classifier.classifier.LogisticRegressionTest2
statmaster.sdu.dk/courses/st111/module03/index.html
object NaiveBayes
NaiveBayes is the companion object for the NaiveBayes class.
object NaiveBayes0
NaiveBayes0 is the companion object for the NaiveBayes0 class.
object NaiveBayesR
NaiveBayesR is the companion object for the NaiveBayesR class.
object NaiveBayesRTest extends App
The NaiveBayesRTest object is used to test the NaiveBayesR class.
The NaiveBayesRTest object is used to test the NaiveBayesR class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.NaiveBayesRTest
object NaiveBayesRTest2 extends App
The NaiveBayesRTest2 object is used to test the NaiveBayesR class.
The NaiveBayesRTest2 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 > runMain scalation.analytics.classifier.NaiveBayesRTest2
object NaiveBayesTest extends App
The NaiveBayesTest object is used to test the 'NaiveBayes' class.
The NaiveBayesTest object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest
object NaiveBayesTest2 extends App
The NaiveBayesTest2 object is used to test the NaiveBayes class.
The NaiveBayesTest2 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 > runMain scalation.analytics.classifier.NaiveBayesTest2
object NaiveBayesTest3 extends App
The NaiveBayesTest3 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest3 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. > runMain scalation.analytics.classifier.NaiveBayesTest3
object NaiveBayesTest4 extends App
The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest4 object is used to test the 'NaiveBayes' class.

See also
docs.roguewave.com/imsl/java/7.3/manual/api/com/imsl/datamining/NaiveBayesClassifierEx2.html > runMain scalation.analytics.classifier.NaiveBayesTest4
archive.ics.uci.edu/ml/datasets/Lenses
object NaiveBayesTest5 extends App
The NaiveBayesTest5 object is used to test the 'NaiveBayes' class.
The NaiveBayesTest5 object is used to test the 'NaiveBayes' class. > runMain scalation.analytics.classifier.NaiveBayesTest5
object NullModelTest extends App
The NullModelTest object is used to test the NullModel class.
The NullModelTest object is used to test the NullModel class. Classify whether to play tennis(1) or not (0). > runMain scalation.analytics.classifier.NullModelTest
object OneBAN
OneBAN is the companion object for the OneBAN class.
object OneBAN0
OneBAN0 is the companion object for the OneBAN0 class.
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 > runMain scalation.analytics.classifier.OneBANTest
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. > runMain scalation.analytics.classifier.OneBANTest2
object OneBANTest3 extends App
The OneBANTest3 object is used to test the OneBAN class.
The OneBANTest3 object is used to test the OneBAN class. > runMain scalation.analytics.classifier.OneBANTest3
object PGMHD3
PGMHD3 is the companion object for the PGMHD3 class.
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 > runMain scalation.analytics.classifier.PGMHD3Test
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. > runMain scalation.analytics.classifier.PGMHD3Test2
object PGMHD3Test3 extends App
The PGMHD3Test3 object is used to test the 'PGMHD3' class.
The PGMHD3Test3 object is used to test the 'PGMHD3' class. > runMain scalation.analytics.classifier.PGMHD3Test3
object PGMHD3cp
PGMHD3cp is the companion object for the PGMHD3cp class.
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 > runMain scalation.analytics.classifier.PGMHD3cpTest
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. > runMain scalation.analytics.classifier.PGMHD3cpTest2
object PGMHD3cpTest3 extends App
The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class.
The PGMHD3cpTest3 object is used to test the 'PGMHD3cp' class. > runMain scalation.analytics.classifier.PGMHD3cpTest3
object PGMHD3fl
PGMHD3fl is the companion object for the PGMHD3fl class.
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 > runMain scalation.analytics.classifier.PGMHD3flTest
object RandomForestTest extends App
The RandomForestTest object is used to test the RandomForest class.
The RandomForestTest object is used to test the RandomForest class. It tests a simple case that does not require a file to be read. > runMain scalation.analytics.classifier.RandomForestTest
object RandomForestTest2 extends App
The RandomForestTest2 object is used to test the RandomForest class.
The RandomForestTest2 object is used to test the RandomForest class. It tests the Random Forest classifier using well-known WineQuality Dataset. > runMain scalation.analytics.classifier.RandomForestTest2
object RandomForestTest3 extends App
The RandomForestTest3 object is used to test the RandomForest class.
The RandomForestTest3 object is used to test the RandomForest class. It tests the Random Forest classifier by specific numbers of trees. > runMain scalation.analytics.classifier.RandomForestTest3
object RandomForestTest4 extends App
The RandomForestTest4 object is used to test the RandomForest class.
The RandomForestTest4 object is used to test the RandomForest class. It tests RF using unseen data. > runMain scalation.analytics.classifier.RandomForestTest4
object Round
The Round object provides methods to round double vectors and matrices into integer vectors and matrices.
object SimpleLDATest extends App
The SimpleLDATest is used to test the SimpleLDA class.
The SimpleLDATest is used to test the SimpleLDA class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.SimpleLDATest
object SimpleLDATest2 extends App
The SimpleLDATest2 is used to test the SimpleLDA class.
The SimpleLDATest2 is used to test the SimpleLDA class. > runMain scalation.analytics.classifier.SimpleLDATest2
object SimpleLogisticRegression
The SimpleLogisticRegression companion object provide factory methods.
object SimpleLogisticRegressionTest extends App
The SimpleLogisticRegressionTest object tests the SimpleLogisticRegression class on the mtcars dataseet.
The SimpleLogisticRegressionTest object tests the SimpleLogisticRegression class on the mtcars dataseet.

See also
www.cookbook-r.com/Statistical_analysis/Logistic_regression/ Answer: b = (-8.8331, 0.4304), n_dev = 43.860, r_dev = 25.533, aic = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest
object SimpleLogisticRegressionTest2 extends App
The SimpleLogisticRegressionTest2 object tests the SimpleLogisticRegression class.
The SimpleLogisticRegressionTest2 object tests the SimpleLogisticRegression 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, aic = 29.533, pseudo_rSq = 0.4178 > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest2
object SimpleLogisticRegressionTest3 extends App
The SimpleLogisticRegressionTest3 is used to test the SimpleLogisticRegression class.
The SimpleLogisticRegressionTest3 is used to test the SimpleLogisticRegression class.

See also
people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html > runMain scalation.analytics.classifier.SimpleLogisticRegressionTest3
object SupportVectorMachineTest extends App
The SupportVectorMachineTest is used to test the SupportVectorMachine class.
The SupportVectorMachineTest is used to test the SupportVectorMachine class. > runMain scalation.analytics.classifier.SupportVectorMachineTest
object SupportVectorMachineTest2 extends App
The SupportVectorMachineTest2 is used to test the SupportVectorMachine class.
object TANBayes
The TANBayes object is the companion object for the TANBayes class.
object TANBayes0
The TANBayes0 object is the companion object for the TANBayes0 class.
object TANBayesTest extends App
The TANBayesTest object is used to test the 'TANBayes' class.
The TANBayesTest object is used to test the 'TANBayes' class. > runMain scalation.analytics.classifier.TANBayesTest
object TANBayesTest2 extends App
The TANBayesTest2 object is used to test the TANBayes0 class.
The TANBayesTest2 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 > runMain scalation.analytics.classifier.TANBayesTest2
object TANBayesTest3 extends App
The TANBayesTest3 object is used to test the TANBayes0 class.
The TANBayesTest3 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. > runMain scalation.analytics.classifier.TANBayesTest3
object TANBayesTest4 extends App
The TANBayesTest4 object is used to test the TANBayes0 class.
The TANBayesTest4 object is used to test the TANBayes0 class. > runMain scalation.analytics.classifier.TANBayesTest4
object TwoBAN_OS
The TwoBAN_OS object is the companion object for the TwoBAN_OS class.
object TwoBAN_OS0
The TwoBAN_OS0 object is the companion object for the TwoBAN_OS0 class.
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 > runMain scalation.analytics.classifier.TwoBAN_OSTest
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. > runMain scalation.analytics.classifier.TwoBAN_OSTest2
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. > runMain scalation.analytics.classifier.TwoBAN_OSTest3

Packages

classifier

package classifier

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Packages

classifier 

package classifier

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classifier