README.md

metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_file: example.pkl
widget:
  structuredData:
    'Unnamed: 32':
      - .nan
      - .nan
      - .nan
    area_mean:
      - 481.9
      - 1130
      - 748.9
    area_se:
      - 30.29
      - 96.05
      - 48.31
    area_worst:
      - 677.9
      - 1866
      - 1156
    compactness_mean:
      - 0.1058
      - 0.1029
      - 0.1223
    compactness_se:
      - 0.01911
      - 0.01652
      - 0.01484
    compactness_worst:
      - 0.2378
      - 0.2336
      - 0.2394
    concave points_mean:
      - 0.03821
      - 0.07951
      - 0.08087
    concave points_se:
      - 0.01037
      - 0.0137
      - 0.01093
    concave points_worst:
      - 0.1015
      - 0.1789
      - 0.1514
    concavity_mean:
      - 0.08005
      - 0.108
      - 0.1466
    concavity_se:
      - 0.02701
      - 0.02269
      - 0.02813
    concavity_worst:
      - 0.2671
      - 0.2687
      - 0.3791
    fractal_dimension_mean:
      - 0.06373
      - 0.05461
      - 0.05796
    fractal_dimension_se:
      - 0.003586
      - 0.001698
      - 0.002461
    fractal_dimension_worst:
      - 0.0875
      - 0.06589
      - 0.08019
    id:
      - 87930
      - 859575
      - 8670
    perimeter_mean:
      - 81.09
      - 123.6
      - 101.7
    perimeter_se:
      - 2.497
      - 5.486
      - 3.094
    perimeter_worst:
      - 96.05
      - 165.9
      - 124.9
    radius_mean:
      - 12.47
      - 18.94
      - 15.46
    radius_se:
      - 0.3961
      - 0.7888
      - 0.4743
    radius_worst:
      - 14.97
      - 24.86
      - 19.26
    smoothness_mean:
      - 0.09965
      - 0.09009
      - 0.1092
    smoothness_se:
      - 0.006953
      - 0.004444
      - 0.00624
    smoothness_worst:
      - 0.1426
      - 0.1193
      - 0.1546
    symmetry_mean:
      - 0.1925
      - 0.1582
      - 0.1931
    symmetry_se:
      - 0.01782
      - 0.01386
      - 0.01397
    symmetry_worst:
      - 0.3014
      - 0.2551
      - 0.2837
    texture_mean:
      - 18.6
      - 21.31
      - 19.48
    texture_se:
      - 1.044
      - 0.7975
      - 0.7859
    texture_worst:
      - 24.64
      - 26.58
      - 26

Model description

[More Information Needed]

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand

Hyperparameter	Value
memory
steps	[('imputer', SimpleImputer()), ('scaler', StandardScaler()), ('model', LogisticRegression())]
verbose	False
imputer	SimpleImputer()
scaler	StandardScaler()
model	LogisticRegression()
imputer__add_indicator	False
imputer__copy	True
imputer__fill_value
imputer__missing_values	nan
imputer__strategy	mean
imputer__verbose	0
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
model__C	1.0
model__class_weight
model__dual	False
model__fit_intercept	True
model__intercept_scaling	1
model__l1_ratio
model__max_iter	100
model__multi_class	auto
model__n_jobs
model__penalty	l2
model__random_state
model__solver	lbfgs
model__tol	0.0001
model__verbose	0
model__warm_start	False

Model Plot

The model plot is below.

Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])

Please rerun this cell to show the HTML repr or trust the notebook.

Evaluation Results

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

Metric	Value
accuracy	0.982456
f1 score	0.982456

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]

Confusion Matrix