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.

Add multiple child nodes to the tree via branchs from node 'n'.

Add child node 'c' to the tree via branch 'v' from node 'n'.

Add the root node to the tree.

Of all the pruning candidates, find the one with the least gain.

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.

Calculate the entropy of the tree as the weighted average over the list of nodes (defualts to leaves).

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 FIX.

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.

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. If no branch found, give maximal decision of current node. Return the best class and its name.

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. If no branch found, give maximal decision of current node. Return the best class and its name.

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 (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 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.

Determine whether all the children of node 'n' are leaf nodes.

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

As part of tree pruning, turn an internal node into a leaf.

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 (feature order) vector.

Print the decision tree using 'prinT' method from Node class.

Prune 'nPrune' nodes from the tree, the ones providing the least gain.

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 counters, if needed.

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).

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

Train the decision tree.

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.

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.