getml.predictors.ScaleGBMRegressor dataclass
ScaleGBMRegressor(
colsample_bylevel: float = 1.0,
colsample_bytree: float = 1.0,
early_stopping_rounds: int = 10,
gamma: float = 0.0,
goss_a: float = 1.0,
goss_b: float = 0.0,
learning_rate: float = 0.1,
max_depth: int = 3,
min_child_weights: float = 1.0,
n_estimators: int = 100,
n_jobs: int = 1,
objective: Literal[
"reg:squarederror"
] = "reg:squarederror",
reg_lambda: float = 1.0,
seed: int = 5843,
)
Bases: _Predictor
Standard gradient boosting regressor that fully supports memory mapping and can be used for datasets that do not fit into memory.
Gradient tree boosting trains an ensemble of decision trees by training each tree to predict the prediction error of all previous trees in the ensemble:
where \(\nabla f_{t,i}\) is the prediction generated by the newest decision tree for sample \(i\) and \(f_{t1,i}\) is the prediction generated by all previous trees, \(L(...)\) is the loss function used and \(y_i\) is the target we are trying to predict.
The regressor implements this general approach by adding two specific components:

The loss function \(L(...)\) is approximated using a Taylor series.

The leaves of the decision tree \(\nabla f_{t,i}\) contain weights that can be regularized.
These weights are calculated as follows:
where \(g_i\) and \(h_i\) are the first and second order derivative of \(L(...)\) w.r.t. \(f_{t1,i}\), \(w_l\) denotes the weight on leaf \(l\) and \(i \in l\) denotes all samples on that leaf.
\(\lambda\) is the regularization parameter reg_lambda
. This hyperparameter can be set by the users or the hyperparameter optimization algorithm to avoid overfitting.
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PARAMETER  DESCRIPTION 

colsample_bylevel  Subsample ratio for the columns used, for each level inside a tree. Note that ScaleGBM grows its trees levelbylevel, not nodebynode. At each level, a subselection of the features will be randomly picked and the best feature for each split will be chosen. This hyperparameter determines the share of features randomly picked at each level. When set to 1, then now such sampling takes place. Decreasing this hyperparameter reduces the likelihood of overfitting. Range: (0, 1] TYPE: 
colsample_bytree  Subsample ratio for the columns used, for each tree. This means that for each tree, a subselection of the features will be randomly chosen. This hyperparameter determines the share of features randomly picked for each tree. Decreasing this hyperparameter reduces the likelihood of overfitting. Range: (0, 1] TYPE: 
early_stopping_rounds  The number of early_stopping_rounds for which we see no improvement on the validation set until we stop the training process. Range: (0, ∞] TYPE: 
gamma  Minimum loss reduction required for any update to the tree. This means that every potential update will first be evaluated for its improvement to the loss function. If the improvement exceeds gamma, the update will be accepted. Increasing this hyperparameter reduces the likelihood of overfitting. Range: [0, ∞] TYPE: 
goss_a  Share of the samples with the largest residuals taken for each tree. If Range: [0, 1] TYPE: 
goss_b  Share of the samples that are not in the The sum of Range: [0, 1] TYPE: 
learning_rate  Learning rate for the gradient boosting algorithm. When a new tree \(\nabla f_{t,i}\) is trained, it will be added to the existing trees \(f_{t1,i}\). Before doing so, it will be multiplied by the learning_rate. Decreasing this hyperparameter reduces the likelihood of overfitting. Range: [0, 1] TYPE: 
max_depth  Maximum allowed depth of the trees. Decreasing this hyperparameter reduces the likelihood of overfitting. Range: [0, ∞] TYPE: 
min_child_weights  Minimum sum of weights needed in each child node for a split. The idea here is that any leaf should have a minimum number of samples in order to avoid overfitting. This very common form of regularizing decision trees is slightly modified to refer to weights instead of number of samples, but the basic idea is the same. Increasing this hyperparameter reduces the likelihood of overfitting. Range: [0, ∞] TYPE: 
n_estimators  Number of estimators (trees). Decreasing this hyperparameter reduces the likelihood of overfitting. Range: [10, ∞] TYPE: 
n_jobs  Number of parallel threads. When set to zero, then the optimal number of threads will be inferred automatically. Range: [0, ∞] TYPE: 
objective  Specify the learning task and the corresponding learning objective. Possible values:
TYPE: 
reg_lambda  L2 regularization on the weights. Please refer to the introductory remarks to understand how this hyperparameter influences your weights. Increasing this hyperparameter reduces the likelihood of overfitting. Range: [0, ∞] TYPE: 
seed  Seed used for random sampling and other random factors. Range: [0, ∞] TYPE: 
validate
validate(params=None)
Checks both the types and the values of all instance variables and raises an exception if something is off.
PARAMETER  DESCRIPTION 

params  A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated. TYPE: 
Note
This method is called at end of the __init__
constructor and every time before the predictor  or a class holding it as an instance variable  is sent to the getML Engine.
Source code in getml/predictors/scale_gbm_regressor.py
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