--- license: apache-2.0 library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: skops-rsihc7ad.pkl widget: - structuredData: Age: - 52 - 52 - 37 Annual income from social welfare programs: - 0 - 0 - 0 Education: - 5 - 2 - 3 Gender: - 0 - 1 - 1 I am currently employed at least part-time: - 1 - 1 - 1 I have a gap in my resume: - 0 - 0 - 0 I have my regular access to the internet: - 1 - 1 - 1 I live with my parents: - 0 - 0 - 0 I read outside of work and school: - 1 - 1 - 1 Income: - 7 - 28 - 100 Lack of concentration: - 0.0 - 0.0 - 0.0 Tiredness: - 0.0 - 1.0 - 1.0 Unemployed: - 0 - 0 - 0 --- # Model description The possible classified predictions are: 'No Mental Illness', 'Yes Mental Illness' The predictors are: 'I am currently employed at least part-time', 'Education' , 'I have my regular access to the internet', 'I live with my parents', 'I have a gap in my resume', 'Income', 'Unemployed', 'I read outside of work and school','Annual income from social welfare programs', 'Lack of concentration', 'Tiredness', 'Age', 'Gender' ## Intended uses & limitations This model follows the limitations of the Apache 2.0 license. ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |----------------------|---------| | covariance_estimator | | | n_components | | | priors | | | shrinkage | | | solver | svd | | store_covariance | False | | tol | 0.0001 |
### Model Plot
LinearDiscriminantAnalysis()
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## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.835821 | | f1 score | 0.835821 | ### Model description/Evaluation Results/Classification report | index | precision | recall | f1-score | support | |--------------------|-------------|----------|------------|-----------| | No Mental Illness | 0.847458 | 0.961538 | 0.900901 | 52 | | Yes Mental Illness | 0.75 | 0.4 | 0.521739 | 15 | | macro avg | 0.798729 | 0.680769 | 0.71132 | 67 | | weighted avg | 0.825639 | 0.835821 | 0.816014 | 67 | # How to Get Started with the Model To use the model run the code in this Google Colab notebook: https://colab.research.google.com/drive/1jBrTTNYGn0dWx3it9CVovc1uFZxNsYHO?usp=sharing