skcla_rf {daltoolboxdp} | R Documentation |
Random Forest Classifier
Description
Implements classification using Random Forest algorithm. This function wraps the RandomForestClassifier from Python's scikit-learn library.
Usage
skcla_rf(
attribute,
slevels,
n_estimators = 100,
criterion = "gini",
max_depth = NULL,
min_samples_split = 2,
min_samples_leaf = 1,
min_weight_fraction_leaf = 0,
max_features = "sqrt",
max_leaf_nodes = NULL,
min_impurity_decrease = 0,
bootstrap = TRUE,
oob_score = FALSE,
n_jobs = NULL,
random_state = NULL,
verbose = 0,
warm_start = FALSE,
class_weight = NULL,
ccp_alpha = 0,
max_samples = NULL,
monotonic_cst = NULL
)
Arguments
attribute |
Target attribute name for model building |
slevels |
List of possible values for classification target |
n_estimators |
Number of trees in random forest |
criterion |
Function name for measuring split quality |
max_depth |
Maximum tree depth value |
min_samples_split |
Minimum samples needed for internal node split |
min_samples_leaf |
Minimum samples needed at leaf node |
min_weight_fraction_leaf |
Minimum weighted fraction value |
max_features |
Number of features to consider for best split |
max_leaf_nodes |
Maximum number of leaf nodes |
min_impurity_decrease |
Minimum impurity decrease needed for split |
bootstrap |
Whether to use bootstrap samples |
oob_score |
Whether to use out-of-bag samples |
n_jobs |
Number of parallel jobs |
random_state |
Seed for random number generation |
verbose |
Whether to enable verbose output |
warm_start |
Whether to reuse previous solution |
class_weight |
Weights associated with classes |
ccp_alpha |
Complexity parameter value for pruning |
max_samples |
Number of samples for training estimators |
monotonic_cst |
Monotonicity constraints for features |
Details
Tree Ensemble
Value
A Random Forest classifier object
skcla_rf
object
Examples
#See an example of using `skcla_rf` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_rf.md