class_docstr: Confidence estimation block, uses inductive conformal predictors (ICPs) for model agnosticity 
class_docstr_tok: ["Confidence","estimation","block",",","uses","inductive","conformal","predictors","(","ICPs",")","for","model","agnosticity"]
code: def __init__(
      self,
      confidence_normalizer: Optional[bool] = False,
      fixed_significance: Optional[float] = None,
      deps: Optional[tuple] = tuple(),
  ):
      super().__init__(deps=deps)
      self.fixed_significance = fixed_significance
      self.confidence_normalizer = confidence_normalizer
      self.validation_size = 100
  
imports: import inspect
  from copy import deepcopy
  from typing import Dict, Tuple, Optional
  from types import SimpleNamespace
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder
  from type_infer.dtype import dtype
  from lightwood.api.types import PredictionArguments
  from lightwood.helpers.ts import add_tn_num_conf_bounds, add_tn_cat_conf_bounds, get_ts_groups
  from lightwood.data import EncodedDs
  from lightwood.analysis.base import BaseAnalysisBlock
  from lightwood.analysis.nc.norm import Normalizer
  from lightwood.analysis.nc.icp import IcpRegressor, IcpClassifier, IcpTSRegressor
  from lightwood.analysis.nc.base import CachedRegressorAdapter, CachedClassifierAdapter, CachedTSAdapter
  from lightwood.analysis.nc.nc import BoostedAbsErrorErrFunc, RegressorNc, ClassifierNc, MarginErrFunc, TSNc, TSAbsErrorErrFunc
  from lightwood.analysis.nc.util import clean_df, set_conf_range, get_numeric_conf_range, get_categorical_conf, get_anomalies, get_ts_conf_range
label_desc: Use image node_id 1 to create a new ICP object from inherited base classes: BaseAnalysisBlock with example: obj = ICP(confidence_normalizer, fixed_significance, deps)