getting started with MLJ

1. loading package and data

    import MLJ:evaluate
    using MLJ,DataFrames
    iris=load_iris()|>DataFrame
    display(first(iris,10))

10×5 DataFrame

Row	sepal_length	sepal_width	petal_length	petal_width	target
	Float64	Float64	Float64	Float64	Cat…
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa
3	4.7	3.2	1.3	0.2	setosa
4	4.6	3.1	1.5	0.2	setosa
5	5.0	3.6	1.4	0.2	setosa
6	5.4	3.9	1.7	0.4	setosa
7	4.6	3.4	1.4	0.3	setosa
8	5.0	3.4	1.5	0.2	setosa
9	4.4	2.9	1.4	0.2	setosa
10	4.9	3.1	1.5	0.1	setosa

2. build DecisionTree model

    y, X = unpack(iris, ==(:target); rng=123);
    Tree = @load DecisionTreeClassifier pkg=DecisionTree
    tree = Tree()
    evaluate(tree, X, y, resampling=CV(shuffle=true),
                 measures=[log_loss, accuracy],
                 verbosity=0)

[ Info: For silent loading, specify `verbosity=0`. 

import MLJDecisionTreeInterface ✔

PerformanceEvaluation object with these fields:
  model, measure, operation, measurement, per_fold,
  per_observation, fitted_params_per_fold,
  report_per_fold, train_test_rows, resampling, repeats
Extract:
┌──────────────────────┬──────────────┬─────────────┬─────────┬─────────────────
│ measure              │ operation    │ measurement │ 1.96*SE │ per_fold       ⋯
├──────────────────────┼──────────────┼─────────────┼─────────┼─────────────────
│ LogLoss(             │ predict      │ 3.12        │ 1.48    │ [2.88, 1.44, 5 ⋯
│   tol = 2.22045e-16) │              │             │         │                ⋯
│ Accuracy()           │ predict_mode │ 0.913       │ 0.041   │ [0.92, 0.96, 0 ⋯
└──────────────────────┴──────────────┴─────────────┴─────────┴─────────────────
                                                                1 column omitted