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.

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.

Classify the value of 'y = f(z)' by evaluating the formula 'y = sigmoid (b dot z)'. Return the best class, its name and quality metric

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

Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability

Clear the total cummulative confusion matrix.

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. Assumes both 'train' and 'confusion' methods have already been called.

Return the labels for the fit. Override when necessary.

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 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.

For a given parameter vector 'b', compute '-2 * Log-Likelihood (-2l)'. '-2l' is the standard measure that follows a Chi-Square distribution.

For a given parameter vector 'b = [b(0)]', compute '-2 * Log-Likelihood (-2l)'. '-2l' is the standard measure that follows a Chi-Square distribution.

the number of data vectors in training-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 coefficient/parameter values.

Compute McFaffen's pseudo R-squared.

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.

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

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).

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

For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing '-2l'. FIX: Use improved BFGS implementation or IRWLS

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.

For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing -2l.

Return default values for binary input data (value count 'vc' set to 2). Also may be used for binning into two categories.