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# # FIT
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"""
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- fit(learner, data; verbosity=1 )
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- fit(learner; verbosity=1 )
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+ fit(learner, data; verbosity=LearnAPI.default_verbosity() )
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+ fit(learner; verbosity=LearnAPI.default_verbosity() )
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Execute the machine learning or statistical algorithm with configuration `learner` using
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the provided training `data`, returning an object, `model`, on which other methods, such
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ŷ = predict(model, Xnew)
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```
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- The signature `fit(learner; verbosity=1 )` (no `data`) is provided by learners that do not
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- generalize to new observations (called *static algorithms*). In that case,
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+ The signature `fit(learner; verbosity=... )` (no `data`) is provided by learners that do
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+ not generalize to new observations (called *static algorithms*). In that case,
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`transform(model, data)` or `predict(model, ..., data)` carries out the actual algorithm
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execution, writing any byproducts of that operation to the mutable object `model` returned
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by `fit`.
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Use `verbosity=0` for warnings only, and `-1` for silent training.
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- See also [`predict `](@ref), [`transform `](@ref), [`inverse_transform `](@ref),
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- [`LearnAPI.functions`](@ref), [`obs`](@ref).
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+ See also [`LearnAPI.default_verbosity `](@ref), [`predict `](@ref), [`transform `](@ref),
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+ [`inverse_transform`](@ref), [` LearnAPI.functions`](@ref), [`obs`](@ref).
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# Extended help
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# New implementations
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Implementation of exactly one of the signatures is compulsory. If `fit(learner;
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- verbosity=1 )` is implemented, then the trait [`LearnAPI.is_static`](@ref) must be
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+ verbosity=... )` is implemented, then the trait [`LearnAPI.is_static`](@ref) must be
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overloaded to return `true`.
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- The signature must include `verbosity`.
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+ The signature must include `verbosity` with [`LearnAPI.default_verbosity()`](@ref) as
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+ default.
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If `data` encapsulates a *target* variable, as defined in LearnAPI.jl documentation, then
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[`LearnAPI.target(data)`](@ref) must be overloaded to return it. If [`predict`](@ref) or
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# # UPDATE AND COUSINS
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"""
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- update(model, data; verbosity=1 , hyperparam_replacements...)
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+ update(model, data; verbosity=LearnAPI.default_verbosity() , hyperparam_replacements...)
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Return an updated version of the `model` object returned by a previous [`fit`](@ref) or
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`update` call, but with the specified hyperparameter replacements, in the form `p1=value1,
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function update end
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"""
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- update_observations(model, new_data; verbosity=1, parameter_replacements...)
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+ update_observations(
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+ model,
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+ new_data;
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+ parameter_replacements...,
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+ verbosity=LearnAPI.default_verbosity(),
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+ )
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Return an updated version of the `model` object returned by a previous [`fit`](@ref) or
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`update` call given the new observations present in `new_data`. One may additionally
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function update_observations end
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"""
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- update_features(model, new_data; verbosity=1, parameter_replacements...)
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+ update_features(
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+ model,
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+ new_data;
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+ parameter_replacements...,
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+ verbosity=LearnAPI.default_verbosity(),
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+ )
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Return an updated version of the `model` object returned by a previous [`fit`](@ref) or
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`update` call given the new features encapsulated in `new_data`. One may additionally
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