--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: model_main_v2_hf.joblib widget: - structuredData: alcohol: - 10.8 - 9.6 - 11.7 chlorides: - 0.171 - 0.095 - 0.063 citric acid: - 0.43 - 0.0 - 0.33 density: - 0.9982 - 0.99854 - 0.99516 fixed acidity: - 10.8 - 8.1 - 9.1 free sulfur dioxide: - 27.0 - 5.0 - 13.0 pH: - 3.17 - 3.36 - 3.26 residual sugar: - 2.1 - 4.1 - 2.05 sulphates: - 0.76 - 0.53 - 0.84 total sulfur dioxide: - 66.0 - 14.0 - 27.0 volatile acidity: - 0.47 - 0.82 - 0.29 --- # Model description This is the best model ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |--------------------------|---------| | bootstrap | True | | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | sqrt | | max_leaf_nodes | | | max_samples | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | monotonic_cst | | | n_estimators | 100 | | n_jobs | | | oob_score | False | | random_state | 0 | | verbose | 0 | | warm_start | False |
### Model Plot
RandomForestClassifier(random_state=0)
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## Evaluation Results | Metric | Value | |----------|---------| | accuracy | 0.7125 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # 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] ``` # citation_bibtex bibtex @inproceedings{...,year={2020}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is not ready to be used in production. # model_description This is a RandomForest Model model trained on wine classification dataset. # confusion_matrix ![confusion_matrix](confusion_matrix.png)