300 - Picking the best model and corresponding hyperparameters using Gridsearch

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  • Опубликовано: 2 окт 2024
  • Code generated in the video can be downloaded from here:
    github.com/bns...
    Picking the best model and corresponding hyperparameters
    using cross validation inside a Gridsearch
    The grid search provided by GridSearchCV exhaustively generates candidates
    from a grid of parameter values specified with the param_grid parameter
    Example:
    param1 = {}
    param1['classifier__n_estimators'] = [10, 50, 100, 250]
    param1['classifier__max_depth'] = [5, 10, 20]
    param1['classifier__class_weight'] = [None, {0:1,1:5}, {0:1,1:10}, {0:1,1:25}]
    param1['classifier'] = [RandomForestClassifier(random_state=42)]
    The GridSearchCV instance when “fitting” on a dataset, all the possible
    combinations of parameter values are evaluated and the best combination is retained.
    cv parameter can be defined for the cross-validation splitting strategy.
    Wisconsin breast cancer example
    Dataset link: www.kaggle.com...

Комментарии • 9

  • @nahid-rl5iu
    @nahid-rl5iu 2 месяца назад

    Hello Sreeni, thanks for the informative tutorials. 12:00 You have defined 'params' that has hyperparameters for all the models. However, for GridsearchCV the 'pipeline' takes only the 'RandomforestClassifier', aren't you supposed to use the 'param1' that has hyperparameters defined for RandomforestClassifier. Correct me if I am wrong. Thanks!!

  • @rs9130
    @rs9130 Год назад +2

    Vision transformers: most awaited video of the century by Sreeni

  • @akhil186
    @akhil186 5 месяцев назад +1

    Hello Sreeni, thanks for the informative tutorials. You have defined 'params' that has hyperparameters for all the models. However, for GridsearchCV the 'pipeline' takes only the 'RandomforestClassifier', aren't you supposed to use the 'param1' that has hyperparameters defined for RandomforestClassifier. Correct me if I am wrong. Thanks!!

    • @nahid-rl5iu
      @nahid-rl5iu 2 месяца назад

      i am also thinking so

  • @josephmonyoro5129
    @josephmonyoro5129 Год назад

    Amazing video thanks alot, now I can cross validation on models and parameters tuning.😁😁😁

  • @HariramG-v7k
    @HariramG-v7k Год назад

    the training of the brat20 dataset in my system is very slow and it showing it exceeds the cpu memory of 10%,can you please give me a solution sir

  • @hakankosebas2085
    @hakankosebas2085 Год назад

    will be there model tutorial like mask rcnn but faster models and for road segmentation in real time

  • @caiyu538
    @caiyu538 Год назад

    Great