MLJ Cheatsheet

Tasks

task = supervised(data=…, types=…, target=…, ignore=…, is_probabilistic=false, verbosity=1)

task = unsupervised(data=…, types=…, ignore=…, verbosity=1)

task.X for inputs (a vector or table)

task.y for target (a vector)

shuffle(task)

task[1:10]

nrows(task)

models(task) is all models adapted to the task

Data and scitypes

Finite{N} has subtypes Multiclass{N} and OrderedFactor{N}.

Infinite has subtypes Continuous and Count

scitype(x) is the scientific type of x

Use schema(X) to get the column scitypes of a table X

coerce(Multiclass, y) attempts coercion of all elements of y into scientific type Multiclass

coerce(Dict(:x1 => Continuous, :x2 => OrderedFactor), X) to coerce columns :x1 and :x2 of X.

Machine construction

Supervised case:

model = KNNRegressor(K=1) and mach = machine(model, X, y) or mach = machine(model, task)

Unsupervised case:

model = OneHotEncoder() and mach = machine(model, X) or mach = machine(model, task)

Inspect all model hyperparameters, even nested ones:

params(model)

Fitting

fit!(mach, rows=:, verbosity=1, force=false)

Inspecting results of fitting

fitted_params(mach) for learned parameters

report(mach) for other results (e.g. feature rankings)

Prediction

Supervised case: predict(mach, Xnew) or predict(mach, rows=:) or predict(mach, task)

Similarly, for probabilistic models: predict_mode, predict_mean and predict_median.

Unsupervised case: transform(mach, rows=:) or inverse_transform(mach, rows), etc.

Resampling strategies

Holdout(fraction_train=…, shuffle=false) for simple holdout

CV(nfolds=6, parallel=true, shuffle=false) for cross-validation

or a list of pairs of row indices:

[(train1, eval1), (train2, eval2), ... (traink, evalk)]

Performance estimation

evaluate(model, X, y, resampling=CV(), measure=..., operation=predict, weights=..., verbosity=1) evaluate!(mach, resampling=Holdout(), measure=..., operation=predict, weights=..., verbosity=1)

Ranges for tuning

If r = range(KNNRegressor(), :K, lower=1, upper = 20, scale=:log) then iterator(r, 6) = [1, 2, 3, 6, 11, 20]

Non-numeric ranges: r = range(model, :parameter, values=…).

Nested ranges: Use dot syntax, as in r = range(EnsembleModel(atom=tree), :(atom.max_depth), ...)

Tuning strategies

Grid(resolution=10, parallel=true) for grid search

Tuning model wrapper

tuned_model = TunedModel(model=…, tuning=Grid(), resampling=Holdout(), measure=…, operation=predict, ranges=…, minimize=true, full_report=true)

Use params(model) to get pattern to match in specifying nested_ranges.

Learning curves

curve = learning_curve!(mach, resolution=30, resampling=Holdout(), measure=…, operation=predict, range=…,, n=1)

If using Plots.jl:

plot(curve.parameter_values, curve.measurements, xlab=curve.parameter_name, xscale=curve.parameter_scale)

Ensemble model wrapper

EnsembleModel(atom=…, weights=Float64[], bagging_fraction=0.8, rng=GLOBAL_RNG, n=100, parallel=true, out_of_bag_measure=[])

Built-in measures

l1, l2, mav, rms, rmsl, rmslp1, rmsp, misclassification_rate, cross_entropy

Transformers

Built-ins include: Standardizer, OneHotEncoder, UnivariateBoxCoxTransformer, FeatureSelector, UnivariateStandardizer

Externals include: PCA (in MultivariateStats), KMeans, KMedoids (in Clustering).

Full list: do models(m -> !m[:is_supervised])