code: def choose_model(
      df,
      model_name=None,
      model_type=None,
      target=None,
      supervised_threshold=3000,
  ):
      """Choose the best model based on the size of the dataset and the model type"""
      training_df = preprocess_data(df)
      model_type = choose_model_type(
          training_df, model_type, target, supervised_threshold
      )
      if model_name is not None:
          assert (
              model_name in MODELS[model_type]
          ), f"model name must be one of {list(MODELS[model_type].keys())}"
          model = MODELS[model_type][model_name]
      else:
          model = None
      if model_type == "unsupervised":
          return train_unsupervised(training_df, model=model)
  
      X_train = training_df.drop(target, axis=1)
      y_train = training_df[target].astype(int)
  
      if model_type == "supervised":
          return train_supervised(X_train, y_train, model=model)
      elif model_type == "semi-supervised":
          return train_semisupervised(X_train, y_train)
  
imports: from mindsdb.integrations.libs.base import BaseMLEngine
  import pandas
  from mindsdb.integrations.handlers.anomaly_detection_handler.utils import train_unsupervised, train_supervised, train_semisupervised
  from joblib import dump, load
  from pyod.models.ecod import ECOD
  from pyod.models.xgbod import XGBOD
  from catboost import CatBoostClassifier
  from pyod.models.lof import LOF
  from pyod.models.knn import KNN
  from pyod.models.pca import PCA
  from xgboost import XGBClassifier
  from sklearn.naive_bayes import GaussianNB
  import numpy
  import os
label_desc: Use image node_id 2 for calling a global function with example usage: choose_model(df, model_name, model_type, target, supervised_threshold) and returns: train_unsupervised, train_supervised, train_semisupervised