README.md

---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: example_model_v1.pkl
widget:
- structuredData:
    Academic Pressure:
    - -0.04606674611568451
    - -0.04606674611568451
    - -0.04606674611568451
    Academic Pressure_na:
    - 2
    - 2
    - 2
    Age:
    - 1.2607895135879517
    - -0.19406381249427795
    - 1.584090232849121
    CGPA:
    - 0.03309895098209381
    - 0.03309895098209381
    - 0.03309895098209381
    CGPA_na:
    - 2
    - 2
    - 2
    City:
    - 98
    - 28
    - 27
    Degree:
    - 77
    - 90
    - 77
    Dietary Habits:
    - 16
    - 21
    - 21
    Family History of Mental Illness:
    - 1
    - 2
    - 2
    Financial Stress:
    - 1.4229286909103394
    - 1.4229286909103394
    - 0.008482186123728752
    Financial Stress_na:
    - 1
    - 1
    - 1
    Gender:
    - 2
    - 2
    - 2
    Have you ever had suicidal thoughts ?:
    - 1
    - 1
    - 2
    Job Satisfaction:
    - 0.8020281791687012
    - 1.5897727012634277
    - -1.5612051486968994
    Job Satisfaction_na:
    - 1
    - 1
    - 1
    Name:
    - 79
    - 269
    - 90
    Profession:
    - 46
    - 15
    - 56
    Sleep Duration:
    - 30
    - 20
    - 30
    Study Satisfaction:
    - 0.019511962309479713
    - 0.019511962309479713
    - 0.019511962309479713
    Study Satisfaction_na:
    - 2
    - 2
    - 2
    Work Pressure:
    - 1.588835597038269
    - -0.7938991189002991
    - -0.7938991189002991
    Work Pressure_na:
    - 1
    - 1
    - 1
    Work/Study Hours:
    - 1.2296171188354492
    - 1.4891586303710938
    - -1.1062564849853516
    Working Professional or Student:
    - 2
    - 2
    - 2
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter           | Value   |
|--------------------------|---------|
| ccp_alpha                | 0.0     |
| class_weight             |         |
| criterion                | gini    |
| max_depth                |         |
| max_features             |         |
| max_leaf_nodes           |         |
| min_impurity_decrease    | 0.0     |
| min_samples_leaf         | 1       |
| min_samples_split        | 2       |
| min_weight_fraction_leaf | 0.0     |
| random_state             |         |
| splitter                 | best    |

</details>

### Model Plot

<style>#sk-container-id-2 {color: black;}#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>DecisionTreeClassifier()</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">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>

## Evaluation Results

| Metric   |    Value |
|----------|----------|
| accuracy | 0.899751 |

# How to Get Started with the Model

[More Information Needed]

# Model Card Authors

This model card is written by following authors:

[More Information Needed]

# Model Card Contact

You can contact the model card authors through following channels:
[More Information Needed]

# Citation

Below you can find information related to citation.

**BibTeX:**
```
[More Information Needed]
```

# citation_bibtex

bibtex
@inproceedings{...,year={2024}}

# get_started_code

import pickle 
with open(dtc_pkl_filename, 'rb') as file: 
    clf = pickle.load(file)

# model_card_authors

Rubanza

# limitations

This model is not ready to be used in production.

# model_description

This is a random forest classifier model trained a mental health dataset.

# eval_method

The model is evaluated using test split, on accuracy