readme: ---
  library_name: sklearn
  tags:
  - sklearn
  - skops
  - tabular-classification
  model_file: model.pkl
  widget:
    structuredData:
      angel_n_rounds:
      - 0.0
      - 0.0
      - 0.0
      pre_seed_n_rounds:
      - 0.0
      - 0.0
      - 0.0
      seed_funding:
      - 1250000.0
      - 800000.0
      - 8000000.0
      seed_n_rounds:
      - 1.0
      - 3.0
      - 1.0
      time_first_funding:
      - 1270.0
      - 1856.0
      - 689.0
      time_till_series_a:
      - 1455.0
      - 1667.0
      - 1559.0
  ---
  
  # Model description
  
  [More Information Needed]
  
  ## Intended uses & limitations
  
  [More Information Needed]
  
  ## Training Procedure
  
  ### Hyperparameters
  
  The model is trained with below hyperparameters.
  
  <details>
  <summary> Click to expand </summary>
  
  | Hyperparameter                                | Value                                                                                              |
  |-----------------------------------------------|----------------------------------------------------------------------------------------------------|
  | memory                                        |                                                                                                    |
  | steps                                         | [('transformation', ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(),<br />                                 ['time_first_funding', 'seed_funding',<br />                                  'time_till_series_a'])])), ('model', LogisticRegression(penalty='none', random_state=0))]                                                                                                    |
  | verbose                                       | False                                                                                              |
  | transformation                                | ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(),<br />                                 ['time_first_funding', 'seed_funding',<br />                                  'time_till_series_a'])])                                                                                                    |
  | model                                         | LogisticRegression(penalty='none', random_state=0)                                                 |
  | transformation__n_jobs                        |                                                                                                    |
  | transformation__remainder                     | drop                                                                                               |
  | transformation__sparse_threshold              | 0.3                                                                                                |
  | transformation__transformer_weights           |                                                                                                    |
  | transformation__transformers                  | [('min_max_scaler', MinMaxScaler(), ['time_first_funding', 'seed_funding', 'time_till_series_a'])] |
  | transformation__verbose                       | False                                                                                              |
  | transformation__verbose_feature_names_out     | True                                                                                               |
  | transformation__min_max_scaler                | MinMaxScaler()                                                                                     |
  | transformation__min_max_scaler__clip          | False                                                                                              |
  | transformation__min_max_scaler__copy          | True                                                                                               |
  | transformation__min_max_scaler__feature_range | (0, 1)                                                                                             |
  | model__C                                      | 1.0                                                                                                |
  | model__class_weight                           |                                                                                                    |
  | model__dual                                   | False                                                                                              |
  | model__fit_intercept                          | True                                                                                               |
  | model__intercept_scaling                      | 1                                                                                                  |
  | model__l1_ratio                               |                                                                                                    |
  | model__max_iter                               | 100                                                                                                |
  | mod