metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
widget:
  structuredData:
    attribute_0: null
    attribute_1: null
    attribute_2: null
    attribute_3: null
    loading: null
    measurement_0: null
    measurement_1: null
    measurement_10: null
    measurement_11: null
    measurement_12: null
    measurement_13: null
    measurement_14: null
    measurement_15: null
    measurement_16: null
    measurement_17: null
    measurement_2: null
    measurement_3: null
    measurement_4: null
    measurement_5: null
    measurement_6: null
    measurement_7: null
    measurement_8: null
    measurement_9: null
    product_code: null

Model description

This is a copy of (tabular-playground)[https://huggingface.co/scikit-learn/tabular-playground] for testing purposes.

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand

Hyperparameter	Value
memory
steps	[('transformation', ColumnTransformer(transformers=[('loading_missing_value_imputer',

                             SimpleImputer(), ['loading']),
                            ('numerical_missing_value_imputer',
                             SimpleImputer(),
                             ['loading', 'measurement_3', 'measurement_4',
                              'measurement_5', 'measurement_6',
                              'measurement_7', 'measurement_8',
                              'measurement_9', 'measurement_10',
                              'measurement_11', 'measurement_12',
                              'measurement_13', 'measurement_14',
                              'measurement_15', 'measurement_16',
                              'measurement_17']),
                            ('attribute_0_encoder', OneHotEncoder(),
                             ['attribute_0']),
                            ('attribute_1_encoder', OneHotEncoder(),
                             ['attribute_1']),
                            ('product_code_encoder', OneHotEncoder(),
                             ['product_code'])])), ('model', DecisionTreeClassifier(max_depth=4))]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |

Model Plot

The model plot is below.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy	0.7888
f1 score	0.7888

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

import pickle 
with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file: 
    clf = pickle.load(file)

Model Card Authors

This model card is written by following authors:

huggingface

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

[More Information Needed]

Tree Plot

Confusion Matrix