llama_0.10.1: - small fixes related to support for algorithm features llama_0.10: - support algorithm features llama_0.9.4: - R devel compatiblity llama_0.9.3: - fix R 4.0 compatiblity llama_0.9.2: - fix bug that caused errors in some pairwise classification cases llama_0.9.1: - fix bug that caused errors with learners that supports weights when only a single class label is present llama_0.9: - stricter argument checking: the number of folds to partition in must be an integer - the functions that generate partitions into train/test now overwrite any existing partitions - automatic tuning of models is now supported through the tuneModel function - mlr 2.5 compatiblity - classification functions can now use learners that predict probabilities - various small bug and reliability fixes llama_0.8: - models computed during cross-validation can be saved by passing save.models to the model builders - various performance improvements, especially in the score computing functions - introduce functions for result analysis: perfScatterPlot, predTable - allow to create train/test splits with bootstrapping - stratification for the train/test split generation functions is now turned off by default - feature selection functionality has been retired - some of the internal APIs have changed -- your code may break if you rely on these llama_0.7.2: - take success (if present) into account when determining best algorithm: if nothing was successful on an instance, set to NA -- this means that vbs may return NA as well - fix bugs wrt cost calculations - fix stupid bug that caused the incorrect best algorithm to be determined in some cases - some addtional small bug fixes llama_0.7.1: - allow vbs/singleBest to operate on test splits to simplify the interface - corrected the implementation of contributions() to handle minimisation and maximisation of performance values correctly llama_0.7: - add regressionPairs model, which predicts the performance difference for each pair of algorithms and makes decisions based on that - use mlr for machine learning algorithms - use original problem features along with predictions in stacked learners