tabular-playground

freddyaboulton/tabular-playground

tabular-playground is a tabular classification model from freddyaboulton. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for sklearn.

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

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Updated Aug 31, 2022

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At a glance

Task: Tabular Classification
Library: sklearn
Access: Public
Created: Aug 31, 2022
Updated: Aug 31, 2022
SHA: 31733db6

Model card

From the Hugging Face model README

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.

<details> <summary> Click to expand </summary>

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))]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |

</details>

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.

<details> <summary> Click to expand </summary>

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

</details>

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

Task: Tabular Classification
Library: sklearn
Created: Aug 31, 2022
Updated: Aug 31, 2022