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metadata
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
      Tiredness:
        - 0
        - 1
        - 1
      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()
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.

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