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  1. README.md +232 -0
  2. config.json +195 -0
  3. confusion_matrix.png +0 -0
  4. feature_importances.png +0 -0
  5. model.pkl +3 -0
  6. tree.png +0 -0
README.md ADDED
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1
+ ---
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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
8
+ widget:
9
+ structuredData:
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+ x0:
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+ ---
131
+
132
+ # Model description
133
+
134
+ This is a Decision Tree Classifier trained on breast cancer dataset and pruned with CCP.
135
+
136
+ ## Intended uses & limitations
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+
138
+ This model is trained for educational purposes.
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+
140
+ ## Training Procedure
141
+
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+ ### Hyperparameters
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+
144
+ The model is trained with below hyperparameters.
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+
146
+ <details>
147
+ <summary> Click to expand </summary>
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+
149
+ | Hyperparameter | Value |
150
+ |--------------------------|---------|
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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 | |
156
+ | 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 |
160
+ | 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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+
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+ </details>
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+
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+ ### Model Plot
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+
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+ The model plot is below.
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+
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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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+
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+ ## Evaluation Results
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+
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+ You can find the details about evaluation process and the evaluation results.
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+
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+
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+
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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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+
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+ # How to Get Started with the Model
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+
186
+ Use the code below to get started with the model.
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+
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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)
193
+ with open("config.json") as f:
194
+ config = json.load(f)
195
+ clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
196
+ ```
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+
198
+
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+ # Model Card Authors
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+
201
+ This model card is written by following authors:
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+
203
+ [More Information Needed]
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+
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+ # Model Card Contact
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+
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+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
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+ # Citation
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+
212
+ Below you can find information related to citation.
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+
214
+ **BibTeX:**
215
+ ```
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+ [More Information Needed]
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+ ```
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+
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+
220
+ # Additional Content
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+
222
+ ## Feature Importances
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+
224
+ ![Feature Importances](feature_importances.png)
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+
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+ ## Tree Splits
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+
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+ ![Tree Splits](tree.png)
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+
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+ ## Confusion Matrix
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+
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+ ![Confusion Matrix](confusion_matrix.png)
config.json ADDED
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confusion_matrix.png ADDED
feature_importances.png ADDED
model.pkl ADDED
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tree.png ADDED