--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: model.pkl widget: structuredData: Age at enrollment: - 20 - 19 - 19 Application mode: - 8 - 6 - 1 Application order: - 5 - 1 - 5 Course: - 2 - 11 - 5 Curricular units 1st sem (approved): - 0 - 6 - 0 Curricular units 1st sem (credited): - 0 - 0 - 0 Curricular units 1st sem (enrolled): - 0 - 6 - 6 Curricular units 1st sem (evaluations): - 0 - 6 - 0 Curricular units 1st sem (grade): - 0 - 14 - 0 Curricular units 1st sem (without evaluations): - 0 - 0 - 0 Curricular units 2nd sem (approved): - 0 - 6 - 0 Curricular units 2nd sem (credited): - 0 - 0 - 0 Curricular units 2nd sem (enrolled): - 0 - 6 - 6 Curricular units 2nd sem (evaluations): - 0 - 6 - 0 Curricular units 2nd sem (grade): - 0 - 13.666666666666666 - 0 Curricular units 2nd sem (without evaluations): - 0 - 0 - 0 Daytime/evening attendance: - 1 - 1 - 1 Debtor: - 0 - 0 - 0 Displaced: - 1 - 1 - 1 Educational special needs: - 0 - 0 - 0 Father's occupation: - 10 - 4 - 10 Father's qualification: - 10 - 3 - 27 GDP: - 1.74 - 0.79 - 1.74 Gender: - 1 - 1 - 1 Inflation rate: - 1.4 - -0.3 - 1.4 International: - 0 - 0 - 0 Marital status: - 1 - 1 - 1 Mother's occupation: - 6 - 4 - 10 Mother's qualification: - 13 - 1 - 22 Nacionality: - 1 - 1 - 1 Previous qualification: - 1 - 1 - 1 Scholarship holder: - 0 - 0 - 0 Tuition fees up to date: - 1 - 0 - 0 Unemployment rate: - 10.8 - 13.9 - 10.8 language: - en pipeline_tag: tabular-classification --- # Model description ### 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 | | n_estimators | 100 | | n_jobs | | | oob_score | False | | random_state | | | verbose | 0 | | warm_start | False |
### Model Plot
RandomForestClassifier()
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## Evaluation Results | Metric | Value | |----------|---------| | accuracy | 0.9041 | | roc_auc | 0.9157 | # How to Get Started with the Model ```py import joblib from skops.hub_utils import download download("sulpha/student_academic_success", "path_to_folder") model = joblib.load( "model.pkl" ) ``` # Model Card Authors This model card is written by following authors: @sulpha # Model Card Contact You can contact the model card authors through following channels: github.com/sulphatet # Citation Below you can find information related to citation. **BibTeX:** ``` Valentim Realinho, Jorge Machado, Luís Baptista, & Mónica V. Martins. (2021). Predict students' dropout and academic success (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5777340 ``` # model_description This is a RandomForest Classifier trained on student academic performance data. # limitations This model is trained for educational purposes. # Confusion Matrix ![Confusion Matrix](confusion_matrix.png)