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()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.LinearDiscriminantAnalysis
LinearDiscriminantAnalysis()
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