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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_file: model.pkl |
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widget: |
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structuredData: |
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x0: |
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- 19.89 |
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- 12.89 |
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- 17.14 |
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x1: |
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- 20.26 |
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- 13.12 |
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- 16.4 |
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x10: |
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- 0.5079 |
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- 0.1532 |
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- 1.046 |
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x11: |
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- 0.8737 |
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- 0.469 |
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- 0.976 |
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x12: |
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- 3.654 |
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- 1.115 |
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- 7.276 |
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x13: |
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- 59.7 |
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- 12.68 |
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- 111.4 |
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x14: |
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- 0.005089 |
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- 0.004731 |
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- 0.008029 |
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x15: |
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- 0.02303 |
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- 0.01345 |
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- 0.03799 |
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x16: |
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- 0.03052 |
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- 0.01652 |
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- 0.03732 |
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x17: |
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- 0.01178 |
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- 0.005905 |
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- 0.02397 |
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x18: |
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- 0.01057 |
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- 0.01619 |
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- 0.02308 |
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x19: |
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- 0.003391 |
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- 0.002081 |
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- 0.007444 |
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x2: |
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- 130.5 |
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- 81.89 |
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- 116.0 |
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x20: |
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- 23.73 |
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- 13.62 |
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- 22.25 |
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x21: |
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- 25.23 |
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- 15.54 |
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- 21.4 |
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x22: |
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- 160.5 |
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- 87.4 |
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- 152.4 |
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x23: |
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- 1646.0 |
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- 577.0 |
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- 1461.0 |
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x24: |
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- 0.1417 |
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- 0.09616 |
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- 0.1545 |
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x25: |
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- 0.3309 |
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- 0.1147 |
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- 0.3949 |
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x26: |
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- 0.4185 |
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- 0.1186 |
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- 0.3853 |
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x27: |
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- 0.1613 |
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- 0.05366 |
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- 0.255 |
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x28: |
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- 0.2549 |
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- 0.2309 |
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- 0.4066 |
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x29: |
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- 0.09136 |
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- 0.06915 |
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- 0.1059 |
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x3: |
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- 1214.0 |
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- 515.9 |
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- 912.7 |
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x4: |
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- 0.1037 |
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- 0.06955 |
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- 0.1186 |
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x5: |
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- 0.131 |
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- 0.03729 |
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- 0.2276 |
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x6: |
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- 0.1411 |
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- 0.0226 |
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- 0.2229 |
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x7: |
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- 0.09431 |
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- 0.01171 |
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- 0.1401 |
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x8: |
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- 0.1802 |
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- 0.1337 |
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- 0.304 |
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x9: |
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- 0.06188 |
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- 0.05581 |
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- 0.07413 |
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--- |
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# Model description |
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This is a Decision Tree Classifier trained on breast cancer dataset and pruned with CCP. |
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## Intended uses & limitations |
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This model is trained for educational purposes. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|--------------------------|---------| |
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| ccp_alpha | 0.0 | |
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| class_weight | | |
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| criterion | gini | |
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| max_depth | | |
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| max_features | | |
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| max_leaf_nodes | | |
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| min_impurity_decrease | 0.0 | |
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| min_impurity_split | | |
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| min_samples_leaf | 1 | |
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| min_samples_split | 2 | |
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| min_weight_fraction_leaf | 0.0 | |
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| random_state | 0 | |
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| splitter | best | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}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;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class="sk-top-container"><div class="sk-container"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2f21b6bf-6c69-42c6-8cc3-1024ae9f4a92" type="checkbox" checked><label class="sk-toggleable__label" for="2f21b6bf-6c69-42c6-8cc3-1024ae9f4a92">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(random_state=0)</pre></div></div></div></div></div> |
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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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|----------|----------| |
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| accuracy | 0.937063 | |
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| f1 score | 0.937063 | |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import joblib |
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import json |
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import pandas as pd |
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clf = joblib.load(model.pkl) |
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with open("config.json") as f: |
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config = json.load(f) |
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
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``` |
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# Additional Content |
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## Feature Importances |
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## Tree Splits |
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## Confusion Matrix |
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