skcla_gb {daltoolboxdp} | R Documentation |
Gradient Boosting Classifier
Description
Implements a classifier using the Gradient Boosting algorithm. This function wraps the GradientBoostingClassifier from Python's scikit-learn library.
Usage
skcla_gb(
attribute,
slevels,
loss = "log_loss",
learning_rate = 0.1,
n_estimators = 100,
subsample = 1,
criterion = "friedman_mse",
min_samples_split = 2,
min_samples_leaf = 1,
min_weight_fraction_leaf = 0,
max_depth = 3,
min_impurity_decrease = 0,
init = NULL,
random_state = NULL,
max_features = NULL,
verbose = 0,
max_leaf_nodes = NULL,
warm_start = FALSE,
validation_fraction = 0.1,
n_iter_no_change = NULL,
tol = 1e-04,
ccp_alpha = 0
)
Arguments
attribute |
Target attribute name for model building |
slevels |
Possible values for the target classification |
loss |
Loss function to be optimized ('log_loss', 'exponential') |
learning_rate |
Learning rate that shrinks the contribution of each tree |
n_estimators |
Number of boosting stages to perform |
subsample |
Fraction of samples to be used for fitting the individual base learners |
criterion |
Function to measure the quality of a split |
min_samples_split |
Minimum number of samples required to split an internal node |
min_samples_leaf |
Minimum number of samples required to be at a leaf node |
min_weight_fraction_leaf |
Minimum weighted fraction of the sum total of weights |
max_depth |
Maximum depth of the individual regression estimators |
min_impurity_decrease |
Minimum impurity decrease required for split |
init |
Estimator object to initialize the model |
random_state |
Random number generator seed |
max_features |
Number of features to consider for best split |
verbose |
Controls verbosity of the output |
max_leaf_nodes |
Maximum number of leaf nodes |
warm_start |
Whether to reuse solution of previous call |
validation_fraction |
Proportion of training data to set aside for validation |
n_iter_no_change |
Used to decide if early stopping will be used |
tol |
Tolerance for early stopping |
ccp_alpha |
Complexity parameter for cost-complexity pruning |
Details
Tree Boosting
Value
A Gradient Boosting classifier object
skcla_gb
object
Examples
#See an example of using `skcla_gb` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_gb.md