can you share examples for each parameter pls

[Sandy4321]

there are many parameters for corels
but in example one few used
can you share examples for each parameter pls
better with demonstration how parameters influence to solution and recommendation how to choose parameters

class CorelsClassifier:
"""Certifiably Optimal RulE ListS classifier.

This class implements the CORELS algorithm, designed to produce human-interpretable, optimal
rulelists for binary feature data and binary classification. As an alternative to other
tree based algorithms such as CART, CORELS provides a certificate of optimality for its 
rulelist given a training set, leveraging multiple algorithmic bounds to do so.

In order to use run the algorithm, create an instance of the `CorelsClassifier` class, 
providing any necessary parameters in its constructor, and then call `fit` to generate
a rulelist. `printrl` prints the generated rulelist, while `predict` provides
classification predictions for a separate test dataset with the same features. To determine 
the algorithm's accuracy, run `score` on an evaluation dataset with labels.
To save a generated rulelist to a file, call `save`. To load it back from the file, call `load`.

Attributes
----------
c : float, optional (default=0.01)
    Regularization parameter. Higher values penalize longer rulelists.

n_iter : int, optional (default=1000)
    Maximum number of nodes (rulelists) to search before exiting.

map_type : str, optional (default="prefix")
    The type of prefix map to use. Supported maps are "none" for no map,
    "prefix" for a map that uses rule prefixes for keys, "captured" for
    a map with a prefix's captured vector as keys.

policy : str, optional (default="lower_bound")
    The search policy for traversing the tree (i.e. the criterion with which
    to order nodes in the queue). Supported criteria are "bfs", for breadth-first
    search; "curious", which attempts to find the most promising node; 
    "lower_bound" which is the objective function evaluated with that rulelist
    minus the default prediction error; "objective" for the objective function
    evaluated at that rulelist; and "dfs" for depth-first search.

verbosity : list, optional (default=["rulelist"])
    The verbosity levels required. A list of strings, it can contain any
    subset of ["rulelist", "rule", "label", "minor", "samples", "progress", "mine", "loud"].

    - "rulelist" prints the generated rulelist at the end.
    - "rule" prints a summary of each rule generated.
    - "label" prints a summary of the class labels.
    - "minor" prints a summary of the minority bound.
    - "samples" produces a complete dump of the rules, label, and/or minor data. You must also provide at least one of "rule", "label", or "minor" to specify which data you want to dump, or "loud" for all data. The "samples" option often spits out a lot of output.
    - "progress" prints periodic messages as corels runs.
    - "mine" prints debug information while mining rules, including each rule as it is generated.
    - "loud" is the equivalent of ["progress", "label", "rule", "mine", "minor"].

ablation : int, optional (default=0)
    Specifies addition parameters for the bounds used while searching. Accepted
    values are 0 (all bounds), 1 (no antecedent support bound), and 2 (no
    lookahead bound).

max_card : int, optional (default=2)
    Maximum cardinality allowed when mining rules. Can be any value greater than
    or equal to 1. For instance, a value of 2 would only allow rules that combine
    at most two features in their antecedents.

min_support : float, optional (default=0.01)
    The fraction of samples that a rule must capture in order to be used. 1 minus
    this value is also the maximum fraction of samples a rule can capture.
    Can be any value between 0.0 and 0.5.