# Quick Start Guide: Optimal Regression Trees

In this example we will use Optimal Regression Trees (ORT) on the yacht hydrodynamics dataset. First we load in the data and split into training and test datasets:

using CSV, DataFrames
"yacht_hydrodynamics.data", DataFrame,
delim=' ',            # file uses ' ' as separators rather than ','
ignorerepeated=true,  # sometimes columns are separated by more than one ' '
:length_beam, :froude, :resistance],
)
308×7 DataFrame
Row │ position  prismatic  length_displacement  beam_draught  length_beam  fr ⋯
│ Float64   Float64    Float64              Float64       Float64      Fl ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │     -2.3      0.568                 4.78          3.99         3.17     ⋯
2 │     -2.3      0.568                 4.78          3.99         3.17
3 │     -2.3      0.568                 4.78          3.99         3.17
4 │     -2.3      0.568                 4.78          3.99         3.17
5 │     -2.3      0.568                 4.78          3.99         3.17     ⋯
6 │     -2.3      0.568                 4.78          3.99         3.17
7 │     -2.3      0.568                 4.78          3.99         3.17
8 │     -2.3      0.568                 4.78          3.99         3.17
⋮  │    ⋮          ⋮               ⋮                ⋮             ⋮          ⋱
302 │     -2.3      0.6                   4.34          4.23         2.73     ⋯
303 │     -2.3      0.6                   4.34          4.23         2.73
304 │     -2.3      0.6                   4.34          4.23         2.73
305 │     -2.3      0.6                   4.34          4.23         2.73
306 │     -2.3      0.6                   4.34          4.23         2.73     ⋯
307 │     -2.3      0.6                   4.34          4.23         2.73
308 │     -2.3      0.6                   4.34          4.23         2.73
2 columns and 293 rows omitted
X = df[:, 1:(end - 1)]
y = df[:, end]
(train_X, train_y), (test_X, test_y) = IAI.split_data(:regression, X, y,
seed=12345)

### Optimal Regression Trees

We will use a GridSearch to fit an OptimalTreeRegressor:

grid = IAI.GridSearch(
IAI.OptimalTreeRegressor(
random_seed=123,
),
max_depth=1:5,
)
IAI.fit!(grid, train_X, train_y)
IAI.get_learner(grid)
Optimal Trees Visualization

We can make predictions on new data using predict:

IAI.predict(grid, test_X)
92-element Vector{Float64}:
0.5705063291139242
0.5705063291139242
13.007272727272728
2.2612121212121212
4.5268000000000015
8.163333333333332
20.91692307692308
0.5705063291139242
0.5705063291139242
2.2612121212121212
⋮
0.5705063291139242
2.2612121212121212
4.5268000000000015
8.163333333333332
13.007272727272728
40.353333333333346
0.5705063291139242
4.5268000000000015
13.007272727272728

We can evaluate the quality of the tree using score with any of the supported loss functions. For example, the $R^2$ on the training set:

IAI.score(grid, train_X, train_y, criterion=:mse)
0.9960433580045623

Or on the test set:

IAI.score(grid, test_X, test_y, criterion=:mse)
0.986575389647429

### Optimal Regression Trees with Hyperplanes

To use Optimal Regression Trees with hyperplane splits (ORT-H), you should set the hyperplane_config parameter:

grid = IAI.GridSearch(
IAI.OptimalTreeRegressor(
random_seed=12345,
hyperplane_config=(sparsity=:all,)
),
max_depth=1:4,
)
IAI.fit!(grid, train_X, train_y)
IAI.get_learner(grid)
Optimal Trees Visualization

Now we can find the performance on the test set with hyperplanes:

IAI.score(grid, test_X, test_y, criterion=:mse)
0.9829672227461212

It looks like the addition of hyperplane splits did not help too much here. It seems that the main variable affecting the target is froude, and so perhaps allowing multiple variables per split in the tree is not that useful for this dataset.

### Optimal Regression Trees with Linear Predictions

To use Optimal Regression Trees with linear regression in the leaves (ORT-L), you should set the regression_features parameter to All() and use the regression_lambda parameter to control the degree of regularization.

grid = IAI.GridSearch(
IAI.OptimalTreeRegressor(
random_seed=123,
max_depth=2,
regression_features=All(),
),
regression_lambda=[0.005, 0.01, 0.05],
)
IAI.fit!(grid, train_X, train_y)
IAI.get_learner(grid)
Optimal Trees Visualization

We can find the performance on the test set:

IAI.score(grid, test_X, test_y, criterion=:mse)
0.982097391565015

We can see that the ORT-L model is much smaller than the models with constant predictions and has similar performance.