text: Title: Optuna Mode doesn't work when tuning LightGBM
  Body: Great package, I love that it supports a wide range of functionalities. 
  
  However, when I try to create an AutoML instance in Optuna mode for Light GBM, it fails and gives the following error message, it works when I use other ML models though:
  
  ```[I 2023-12-11 18:14:57,186] A new study created in memory with name: no-name-b35aa835-4503-402c-ad99-b0130c103afb
  [W 2023-12-11 18:14:57,553] Trial 0 failed with parameters: {'learning_rate': 0.1, 'num_leaves': 1598, 'lambda_l1': 2.840098794801191e-06, 'lambda_l2': 3.0773599420974e-06, 'feature_fraction': 0.8613105322932351, 'bagging_fraction': 0.970697557159987, 'bagging_freq': 7, 'min_data_in_leaf': 36, 'extra_trees': False} because of the following error: The value None could not be cast to float..
  [W 2023-12-11 18:14:57,554] Trial 0 failed with value None.
  ```
  
  These are the parameter settings for AutoML:
  ```
  automl = AutoML(
      mode="Optuna",
      eval_metric="f1",
      golden_features=False,
      ml_task='binary_classification',
      kmeans_features=False,
      start_random_models=1,
      stack_models=False,
      train_ensemble=False,
      optuna_time_budget=100,
      optuna_verbose=True,
      features_selection=False,
      algorithms=["LightGBM"],
      validation_strategy={
          "validation_type": "kfold",
          "k_folds": 3,
          "shuffle": True,
          "stratify": True,
      }
  ```
  It would be great if you could help me with this.