metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: model.pkl
widget:
structuredData:
x0:
- 19.89
- 12.89
- 17.14
x1:
- 20.26
- 13.12
- 16.4
x10:
- 0.5079
- 0.1532
- 1.046
x11:
- 0.8737
- 0.469
- 0.976
x12:
- 3.654
- 1.115
- 7.276
x13:
- 59.7
- 12.68
- 111.4
x14:
- 0.005089
- 0.004731
- 0.008029
x15:
- 0.02303
- 0.01345
- 0.03799
x16:
- 0.03052
- 0.01652
- 0.03732
x17:
- 0.01178
- 0.005905
- 0.02397
x18:
- 0.01057
- 0.01619
- 0.02308
x19:
- 0.003391
- 0.002081
- 0.007444
x2:
- 130.5
- 81.89
- 116
x20:
- 23.73
- 13.62
- 22.25
x21:
- 25.23
- 15.54
- 21.4
x22:
- 160.5
- 87.4
- 152.4
x23:
- 1646
- 577
- 1461
x24:
- 0.1417
- 0.09616
- 0.1545
x25:
- 0.3309
- 0.1147
- 0.3949
x26:
- 0.4185
- 0.1186
- 0.3853
x27:
- 0.1613
- 0.05366
- 0.255
x28:
- 0.2549
- 0.2309
- 0.4066
x29:
- 0.09136
- 0.06915
- 0.1059
x3:
- 1214
- 515.9
- 912.7
x4:
- 0.1037
- 0.06955
- 0.1186
x5:
- 0.131
- 0.03729
- 0.2276
x6:
- 0.1411
- 0.0226
- 0.2229
x7:
- 0.09431
- 0.01171
- 0.1401
x8:
- 0.1802
- 0.1337
- 0.304
x9:
- 0.06188
- 0.05581
- 0.07413
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
