metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: skops
model_file: examplej.skops
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 (J).

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__keep_empty_features	False
imputer__missing_values	nan
imputer__strategy	mean
imputer__verbose	deprecated
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())])

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Evaluation Results

[More Information Needed]

How to Get Started with the Model

[More Information Needed]

Model Card Authors

This model card is written by following authors:

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Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

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BibTeX:

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