|
--- |
|
library_name: sklearn |
|
tags: |
|
- sklearn |
|
- skops |
|
- tabular-classification |
|
model_file: example.pkl |
|
widget: |
|
structuredData: |
|
area error: |
|
- 30.29 |
|
- 96.05 |
|
- 48.31 |
|
compactness error: |
|
- 0.01911 |
|
- 0.01652 |
|
- 0.01484 |
|
concave points error: |
|
- 0.01037 |
|
- 0.0137 |
|
- 0.01093 |
|
concavity error: |
|
- 0.02701 |
|
- 0.02269 |
|
- 0.02813 |
|
fractal dimension error: |
|
- 0.003586 |
|
- 0.001698 |
|
- 0.002461 |
|
mean area: |
|
- 481.9 |
|
- 1130.0 |
|
- 748.9 |
|
mean compactness: |
|
- 0.1058 |
|
- 0.1029 |
|
- 0.1223 |
|
mean concave points: |
|
- 0.03821 |
|
- 0.07951 |
|
- 0.08087 |
|
mean concavity: |
|
- 0.08005 |
|
- 0.108 |
|
- 0.1466 |
|
mean fractal dimension: |
|
- 0.06373 |
|
- 0.05461 |
|
- 0.05796 |
|
mean perimeter: |
|
- 81.09 |
|
- 123.6 |
|
- 101.7 |
|
mean radius: |
|
- 12.47 |
|
- 18.94 |
|
- 15.46 |
|
mean smoothness: |
|
- 0.09965 |
|
- 0.09009 |
|
- 0.1092 |
|
mean symmetry: |
|
- 0.1925 |
|
- 0.1582 |
|
- 0.1931 |
|
mean texture: |
|
- 18.6 |
|
- 21.31 |
|
- 19.48 |
|
perimeter error: |
|
- 2.497 |
|
- 5.486 |
|
- 3.094 |
|
radius error: |
|
- 0.3961 |
|
- 0.7888 |
|
- 0.4743 |
|
smoothness error: |
|
- 0.006953 |
|
- 0.004444 |
|
- 0.00624 |
|
symmetry error: |
|
- 0.01782 |
|
- 0.01386 |
|
- 0.01397 |
|
texture error: |
|
- 1.044 |
|
- 0.7975 |
|
- 0.7859 |
|
worst area: |
|
- 677.9 |
|
- 1866.0 |
|
- 1156.0 |
|
worst compactness: |
|
- 0.2378 |
|
- 0.2336 |
|
- 0.2394 |
|
worst concave points: |
|
- 0.1015 |
|
- 0.1789 |
|
- 0.1514 |
|
worst concavity: |
|
- 0.2671 |
|
- 0.2687 |
|
- 0.3791 |
|
worst fractal dimension: |
|
- 0.0875 |
|
- 0.06589 |
|
- 0.08019 |
|
worst perimeter: |
|
- 96.05 |
|
- 165.9 |
|
- 124.9 |
|
worst radius: |
|
- 14.97 |
|
- 24.86 |
|
- 19.26 |
|
worst smoothness: |
|
- 0.1426 |
|
- 0.1193 |
|
- 0.1546 |
|
worst symmetry: |
|
- 0.3014 |
|
- 0.2551 |
|
- 0.2837 |
|
worst texture: |
|
- 24.64 |
|
- 26.58 |
|
- 26.0 |
|
--- |
|
|
|
# Model description |
|
|
|
[More Information Needed] |
|
|
|
## Intended uses & limitations |
|
|
|
[More Information Needed] |
|
|
|
## Training Procedure |
|
|
|
### Hyperparameters |
|
|
|
The model is trained with below 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 |
|
|
|
The model plot is below. |
|
|
|
<style>#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 {color: black;background-color: white;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 pre{padding: 0;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-toggleable {background-color: white;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 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-97b030ee-c64c-4db5-9efe-cbd2bb287e30 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-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-item {z-index: 1;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-parallel-item:only-child::after {width: 0;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 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;position: relative;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30 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-97b030ee-c64c-4db5-9efe-cbd2bb287e30 div.sk-text-repr-fallback {display: none;}</style><div id="sk-97b030ee-c64c-4db5-9efe-cbd2bb287e30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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="ec886b6d-6d45-42b7-8a78-6ee853fec0cd" type="checkbox" checked><label for="ec886b6d-6d45-42b7-8a78-6ee853fec0cd" 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 |
|
|
|
You can find the details about evaluation process and the evaluation results. |
|
|
|
|
|
|
|
| Metric | Value | |
|
|----------|---------| |
|
|
|
# How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
```python |
|
import joblib |
|
import json |
|
import pandas as pd |
|
clf = joblib.load(example.pkl) |
|
with open("config.json") as f: |
|
config = json.load(f) |
|
print(clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))) |
|
``` |
|
|
|
</details> |
|
|
|
|
|
# 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] |
|
``` |
