code: def test_baseline_with_api():
      train_data, test_data, threshold = get_dataset("classification")
  
      # task selection, initialisation of the framework
      baseline_model = Fedot(problem="classification")
  
      # fit model without optimisation - single XGBoost node is used
      baseline_model.fit(
          features=train_data,
          target="target",
          predefined_model="xgboost",
      )
  
      # evaluate the prediction with test data
      prediction = baseline_model.predict_proba(features=test_data)
  
      assert len(prediction) == len(test_data.target)
  
      # evaluate quality metric for the test sample
      baseline_metrics = baseline_model.get_metrics(metric_names="f1")
  
      assert baseline_metrics["f1"] > 0
  
file_path: FEDOT/test/unit/api/test_main_api.py
imports: import numpy
  import pandas
  import pytest
  from fedot import Fedot
  from fedot.api.api_utils.api_data import ApiDataProcessor
  from fedot.core.data.data import InputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.data.datasets import data_with_binary_features_and_categorical_target, get_dataset, load_categorical_multidata, get_split_data_paths, get_split_data, get_multimodal_ts_data, load_categorical_unimodal
label_desc: Use image node_id 6 for calling a global function with example usage: test_baseline_with_api() without return types