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Model description
This is a Decision Tree Classifier trained on breast cancer dataset and pruned with CCP.
Intended uses & limitations
This model is trained for educational purposes.
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|---|---|
| ccp_alpha | 0.0 |
| class_weight | |
| criterion | gini |
| max_depth | |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_impurity_split | |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| random_state | 0 |
| splitter | best |
Model Plot
The model plot is below.
DecisionTreeClassifier(random_state=0)
Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|---|
| accuracy | 0.937063 |
| f1 score | 0.937063 |
How to Get Started with the Model
Use the code below to get started with the model.
import joblib
import json
import pandas as pd
clf = joblib.load(model.pkl)
with open("config.json") as f:
config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
Additional Content
Feature Importances
Tree Splits
Confusion Matrix
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