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Within machine learning, the supervised learning field aims at modeling theinput-output relationship of a system, from past observations of its behavior.Decision trees characterize the input-output relationship through a series ofnested $if-then-else$ questions, the testing nodes, leading to a set ofpredictions, the leaf nodes. Several of such trees are often combined togetherfor state-of-the-art performance: random forest ensembles average thepredictions of randomized decision trees trained independently in parallel,while tree boosting ensembles train decision trees sequentially to refine thepredictions made by the previous ones. The emergence of new applicationsrequires scalable supervised learning algorithms in terms of computationalpower and memory space with respect to the number of inputs, outputs, andobservations without sacrificing accuracy. In this thesis, we identify threemain areas where decision tree methods could be improved for which we provideand evaluate original algorithmic solutions: (i) learning over high dimensionaloutput spaces, (ii) learning with large sample datasets and stringent memoryconstraints at prediction time and (iii) learning over high dimensional sparseinput spaces.

Exploiting random projections and sparsity with random forests and  gradient boosting methods -- Application to multi-label and multi-output  learning, random forest model compression and leveraging input sparsity