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---
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
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|----------------------|---------|
| covariance_estimator | |
| n_components | |
| priors | |
| shrinkage | |
| solver | svd |
| store_covariance | False |
| tol | 0.0001 |
</details>
### Model Plot
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearDiscriminantAnalysis()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">LinearDiscriminantAnalysis</label><div class="sk-toggleable__content"><pre>LinearDiscriminantAnalysis()</pre></div></div></div></div></div>
## 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
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