R2 = range(forest, :bagging_fraction, lower=0.4, upper=1. In this case we will specify a goal for the total number of grid points: r1 = range(forest, :(atom.n_subfeatures), lower=1, upper=9) Generate the Uns so that exactly one point falls in each horizontal or vertical strip (of area 1/N). Out_of_bag_measure = Any) for nested hyperparameters are specified using dot syntax. Stratification for d > 1: Latin hypercube sampling. The forest model below has another model, namely a DecisionTreeRegressor, as a hyperparameter: tree = DecisionTreeRegressor()įorest = EnsembleModel(atom=tree) DeterministicEnsembleModel( Per_fold =, ],) 5 Tuning multiple nested hyperparameters Selection_heuristic = MLJTuning.NaiveSelection(nothing),Īcceleration_resampling = CPU1 = , Self_tuning_tree = TunedModel(model=tree, LATIN HYPERCUBE SAMPLING 1.1 Independent Variables Latin hypercube sampling (LHS) is a stratified random sampling technique in which a sample of size N from mul-tiple (continuous) variables is drawn such that for each individual variable the sample is (marginally) maximally stratified (McKay et al. To do so we will use the simplest range object, a one-dimensional range object constructed using the range method: r = range(tree, :min_purity_increase, lower=0.001, upper=1.0, scale=:log) Merge_purity_threshold = 1.0) tune min_purity_increase in the model above, using a grid-search. Tree = DecisionTreeRegressor verbosity=0 DecisionTreeRegressor( Tuning a single hyperparameter using a grid search (regression example) using MLJ For a complete list of options see the TunedModel doc-string below. In this way the wrapped model may be viewed as a "self-tuning" version of the unwrapped model.įor in-depth overview of tuning in MLJ, or for implementation details, see the MLJTuning documentation. To predict using the optimal model, one just calls predict(mach, Xnew). In MCS we obtain a sample in a purely random fashion whereas in LHS we obtain a pseudo-random sample, that is a sample that mimics a random structure. After wrapping a model in a tuning strategy and binding the wrapped model to data in a machine, mach, calling fit!(mach) instigates a search for optimal model hyperparameters, within a specified range, and then uses all supplied data to train the best model. Monte Carlo Sampling (MCS) and Latin Hypercube Sampling (LHS) are two methods of sampling from a given probability distribution. In MLJ, hyperparameter optimization, also known as model tuning, is implemented as a model wrapper.
Also available is the tree Parzen strategy for a complete list, see here. Below we illustrate hyperparameter optimisation using the Grid, RandomSearch and LatinHypercube tuning strategies.
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