Compute the accuracy of the classification, i.e., the fraction of correct classifications. Note, the correct classifications 'tp_i' are in the main diagonal of the confusion matrix.

Compute the 'AIC' for the given Bayesian Network structure and data.

Compute the conditional mutual information matrix.

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

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.

Given a discrete data vector 'z', classify it returning the class number (0, ..., k-1) with the highest relative posterior probability. Return the best class, its name and its relative probability.

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

Given a new continuous data vector 'z', determine which class it fits into, returning the best class, its name and its relative probability. Override in classes that require precise real values for classification.

Clear the total cummulative confusion matrix.

Compute conditional mutual information matrix given the probability of 'y' and joint probabilities of 'Xy' and 'XyZ', where 'y' is the class, and 'X' & 'Z' are features.

Compute the parent of each feature based on the correlation matrix. Feature x_i is only a possible candidate for parent of feature x_j if i < j.

Compute the value counts of each parent feature based on the parent vector. Let 1 be the default value count when there is no parent.

Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is

yp 0 1 ---------- y 0 | tn fp | 1 | fn tp | ----------

Note: ScalaTion's confusion matrix is Actual × Predicted, but to swap the position of actual 'y' (rows) with predicted 'yp' (columns) simply use 'cmat.t', the transpose of 'cmat'.

Contract the actual class 'yy' vector versus the predicted class 'yp' vector.

Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method slices out instances/rows to form the test dataset.

Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.

Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

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

Compute the F1-measure, i.e., the harmonic mean of the precision and recall.

Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.

Format a double value.

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.

Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.

Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.

the set of features to turn on or off. All features are on by default. Used for feature selection.

Return the parent.

Return the response (class label) vector.

Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class. Override to correspond to 'fitLabel'.

Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

Compute the Log-Likelihood for the given Bayesian Network structure and data.

the number of data vectors in training/test-set (# rows)

the training-set size as a Double

An optional reference to an ontological concept

An optional name for the model (or modeling technique)

the number of features/variables (# columns)

the feature-set size as a Double

Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ... k-1}. -------------------------------------------------------------------------- Precision is the fraction classified as true that are actually true. Recall (sensitivity) is the fraction of the actually true that are classified as true. Specificity is the fraction of the actually false that are classified as false. -------------------------------------------------------------------------- Note, for 'k = 2', ordinary precision 'p', recall 'r' and specificity 's' will correspond to the last elements in the 'pv', 'rv' and 'sv' micro vectors.

Return the vector of model parameter values.

Print the class probabilities.

Compute the Efron's pseudo R-squared value. Override to McFadden's, etc.

Return a basic report on the trained model.

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

Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.

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

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

the random number stream {0, 1, ..., 999} to be used

Produce a summary report with diagnostics and the overall quality of fit.

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.

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.

Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).

Toggle the value of the 'smooth' property.

Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.

Train the classifier by computing the probabilities for C, and the conditional probabilities for X_j. This is the quick version that uses the "subtraction" method to achieve efficiency.

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.

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.

Increment frequency counters used in CMI calculations based on the 'i'th row of the data matrix.

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

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

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