Skip to content

Hyperparameters

All hyperparameters are exposed as flat keyword arguments on TSLRegressor (and the TSL.fit(...) classmethod), and mapped onto the nested Rust builders: TSLBoostedParams → StagePredictorParams → GridTensorParams (SplitStrategyParams, RefinementStrategyParams). Defaults below are the TSLRegressor constructor defaults.

Boosting (forest level)

Param Default Meaning
epochs 10 number of boosting rounds (stages \(R\)); each fits the current residual
decay 1.0 multiply n_iter by this after epoch 1 (<1 makes later stages coarser; 1.0 = off)
seed 42 RNG seed — fits are deterministic given the seed
verbosity 1 log verbosity
visualdb None path to an evo-logging SQLite DB; see Visualization dashboard

Bag & aggregation (stage level)

Param Default Meaning
n_trees 10 grid tensors fit per stage (\(n_{\text{grids}}\)); more reduces variance
similarity_threshold 0.0 trim \(\xi\) — keep top \(\lceil(1-\xi)n_{\text{trees}}\rceil\) bags by similarity (0 keeps all)
bagged False enable the bagged-aggregation path

The aggregation mode (Mean / GeometricMean / Combined) is set internally; see Bagging & aggregation.

Grid refinement (per grid)

Param Default Meaning
n_iter 10 split budget per grid (\(T\)); larger captures more structure
split_strategy "random" "random", "best_split", or "top_k"
split_try 10 candidate split positions sampled per (feature, interval)
colsample_bytree 0.8 fraction of features sampled per split proposal
min_interval_samples 1 minimum observations on each side of a split
min_split_loss 0.0 minimum error reduction to accept a split
complexity_penalty 0.0 penalty discouraging additional splits
top_k 10 (for top_k) sample from the top-\(k\) candidates
must_fill_all_k True (for top_k) require all \(k\) slots filled

Choosing a split strategy

random (the default) is usually best for speed and generalizes well; best_split can help on small datasets where exhaustively picking the top split matters.

Refinement solver (per node)

Param Default Meaning
refinement_strategy "l2" "l2" (weights \(w_i=1\)) or "huber" (robust weights)
alpha 0.0 ridge regularization \(\alpha\) on the bin update (raise if overfitting)
tilt_tau 0.01 \(\ell_2\) coupling \(\tau\) between the \(u_+\) and \(u_-\) updates
tilt_rho 0.0 \(\ell_1\) coupling \(\rho\) on \((u_+ - u_-)\)

These feed the closed-form 2×2 solver.

Advanced / experimental

Param Default Meaning
prior_sample_size 0.0 parent-anchoring strength (\(\tau_0\)); the default 0.0 disables anchoring
update_clamp inf cap on update magnitude; the default inf imposes no extra cap

Note

Leave the two advanced parameters at their defaults unless you are experimenting — the defaults shown are the no-op values. The positivity clamp \([v_{\min}, v_{\max}]\) in the solver is separate and always active.

A reasonable starting point

TSLRegressor(
    epochs=5,            # a few stages
    n_trees=16,          # moderate bagging
    n_iter=30,           # enough splits to capture structure
    split_try=16,
    colsample_bytree=0.8,
    alpha=0.01,          # light ridge
    seed=0,
)

Tune epochs, n_iter, and n_trees first; reach for alpha (and min_split_loss) if the model overfits, and similarity_threshold if bagged components disagree.