metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: pickle
model_file: hw4_mmcar25_classifier.pkl
widget:
  - structuredData:
      Age:
        - .nan
        - 39
        - 34
      Contract Length_Annual:
        - true
        - true
      Contract Length_Monthly:
        - false
        - false
      Contract Length_Quarterly:
        - false
        - false
      CustomerID:
        - 385551
        - 261335
        - .nan
      Gender_Female:
        - false
      Gender_Male:
        - false
        - true
      Last Interaction:
        - 20
        - 9
        - 12
      Payment Delay:
        - 19
        - 7
        - .nan
      Subscription Type_Basic:
        - true
      Subscription Type_Premium:
        - false
        - false
        - false
      Subscription Type_Standard:
        - false
        - false
        - true
      Support Calls:
        - 0
        - 0
        - 8
      Tenure:
        - 43
        - 31
        - 17
      Total Spend:
        - 914.15
        - 657.3
        - 511.21
      Usage Frequency:
        - 23
        - 28
        - .nan

Model description

[More Information Needed]

Intended uses & limitations

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Training Procedure

[More Information Needed]

Hyperparameters

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Hyperparameter	Value
memory
steps	[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())]
verbose	False
Imputer	SimpleImputer()
rf	RandomForestClassifier()
Imputer__add_indicator	False
Imputer__copy	True
Imputer__fill_value
Imputer__keep_empty_features	False
Imputer__missing_values	nan
Imputer__strategy	mean
rf__bootstrap	True
rf__ccp_alpha	0.0
rf__class_weight
rf__criterion	gini
rf__max_depth
rf__max_features	sqrt
rf__max_leaf_nodes
rf__max_samples
rf__min_impurity_decrease	0.0
rf__min_samples_leaf	1
rf__min_samples_split	2
rf__min_weight_fraction_leaf	0.0
rf__monotonic_cst
rf__n_estimators	100
rf__n_jobs
rf__oob_score	False
rf__random_state
rf__verbose	0
rf__warm_start	False

Model Plot

Pipeline(steps=[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())])

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Evaluation Results

Metric	Value
f1 score	0.983605
accuracy	0.981512

How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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Model Card Contact

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Citation

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BibTeX:

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eval_method

The model is evaluated using test split, on accuracy and f1.