README.md

metadata

license: mit
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
      - 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
      - 1156
    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

Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

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_samples_leaf	1
min_samples_split	2
min_weight_fraction_leaf	0.0
random_state
splitter	best

Model Plot

The model plot is below.

DecisionTreeClassifier()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy	0.94152
f1 score	0.94152

How to Get Started with the Model

[More Information Needed]

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]

citation_bibtex

bibtex @inproceedings{...,year={2020}}

get_started_code

import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file)

model_card_authors

skops_user

limitations

This model is not ready to be used in production.

model_description

This is a DecisionTreeClassifier model trained on breast cancer dataset.

eval_method

The model is evaluated using test split, on accuracy and F1 score with macro average.

confusion_matrix