class_docstr: ```
  Abstract class used to tune and train Keras models. The fit method is
  an abstract method and must be implemented by subclasses.
  ```
class_docstr_tok: ["``","`","Abstract","class","used","to","tune","and","train","Keras","models",".","The","fit","method","is","an","abstract","method","and","must","be","implemented","by","subclasses",".","``","`"]
docstr: ```
  alias for self.validate().
  Returns confusion matrix and optionally prints
  a classification report.
  This is currently only supported for binary and multiclass
  classification, not multilabel classification.
  
  By default, this uses val_data, as supplied to ktrain.get_learner().
  Other validation or test data can be optionally be supplied as argument via <test_data> argument.
  Supply class_names to include labels instead of intenger class integer values in classification report.
  Args:
    test_data(Dataset|np.ndarray): test or validation data.  If None, self.val_data is used.
    print_report(bool): If True, classification report will be printed. If False, report will be saved to CSV
                        at save_path. Not applicable to regression models.
                        Not applicable to regression models.
    save_path(str): Classification report will be saved to this file path/name if print_report=False
                    Not applicable to regression models.
    class_names(list): list of class names to be used in classification report instead of
                       class integer IDs.
  ```
docstr_tok: ["``","`","alias","for","self.validate","(",")",".","Returns","confusion","matrix","and","optionally","prints","a","classification","report",".","This","is","currently","only","supported","for","binary","and","multiclass","classification",",","not","multilabel","classification",".","By","default",",","this","uses","val_data",",","as","supplied","to","ktrain.get_learner","(",")",".","Other","validation","or","test","data","can","be","optionally","be","supplied","as","argument","via","<","test_data",">","argument",".","Supply","class_names","to","include","labels","instead","of","intenger","class","integer","values","in","classification","report",".","Args",":","test_data","(","Dataset|np.ndarray",")",":","test","or","validation","data",".","If","None",",","self.val_data","is","used",".","print_report","(","bool",")",":","If","True",",","classification","report","will","be","printed",".","If","False",",","report","will","be","saved","to","CSV","at","save_path",".","Not","applicable","to","regression","models",".","Not","applicable","to","regression","models",".","save_path","(","str",")",":","Classification","report","will","be","saved","to","this","file","path\/name","if","print_report=False","Not","applicable","to","regression","models",".","class_names","(","list",")",":","list","of","class","names","to","be","used","in","classification","report","instead","of","class","integer","IDs",".","``","`"]
code: def evaluate(
      self,
      test_data=None,
      print_report=True,
      save_path="ktrain_classification_report.csv",
      class_names=[],
  ):
      """
      ```
      alias for self.validate().
      Returns confusion matrix and optionally prints
      a classification report.
      This is currently only supported for binary and multiclass
      classification, not multilabel classification.
  
      By default, this uses val_data, as supplied to ktrain.get_learner().
      Other validation or test data can be optionally be supplied as argument via <test_data> argument.
      Supply class_names to include labels instead of intenger class integer values in classification report.
      Args:
        test_data(Dataset|np.ndarray): test or validation data.  If None, self.val_data is used.
        print_report(bool): If True, classification report will be printed. If False, report will be saved to CSV
                            at save_path. Not applicable to regression models.
                            Not applicable to regression models.
        save_path(str): Classification report will be saved to this file path/name if print_report=False
                        Not applicable to regression models.
        class_names(list): list of class names to be used in classification report instead of
                           class integer IDs.
      ```
      """
      return self.validate(
          val_data=test_data,
          print_report=print_report,
          save_path=save_path,
          class_names=class_names,
      )
  
imports: from .None import utils
  from .graph.predictor import LinkPredictor, NodePredictor
  from .graph.preprocessor import LinkPreprocessor, NodePreprocessor
  from .imports import *
  from .lroptimize.lrfinder import *
  from .tabular.predictor import TabularPredictor
  from .tabular.preprocessor import TabularPreprocessor
  from .text.ner.predictor import NERPredictor
  from .text.ner.preprocessor import NERPreprocessor
  from .text.predictor import TextPredictor
  from .text.preprocessor import BERTPreprocessor, TextPreprocessor, TransformersPreprocessor
  from .vision.predictor import ImagePredictor
  from .vision.preprocessor import ImagePreprocessor
label_desc: Use image node_id 5 for calling the Learner obj's underlying member method code with example usage: obj.evaluate(test_data, print_report, save_path, class_names) and returns: self