---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: hw4_mmcar25_classifier.pkl
widget:
- structuredData:
Age:
- .nan
- 39.0
- 34.0
Contract Length_Annual:
- true
- true
Contract Length_Monthly:
- false
- false
Contract Length_Quarterly:
- false
- false
CustomerID:
- 385551.0
- 261335.0
- .nan
Gender_Female:
- false
Gender_Male:
- false
- true
Last Interaction:
- 20.0
- 9.0
- 12.0
Payment Delay:
- 19.0
- 7.0
- .nan
Subscription Type_Basic:
- true
Subscription Type_Premium:
- false
- false
- false
Subscription Type_Standard:
- false
- false
- true
Support Calls:
- 0.0
- 0.0
- 8.0
Tenure:
- 43.0
- 31.0
- 17.0
Total Spend:
- 914.15
- 657.3
- 511.21
Usage Frequency:
- 23.0
- 28.0
- .nan
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
[More Information Needed]
### Hyperparameters
Click to expand
| 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 |
Pipeline(steps=[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('Imputer', SimpleImputer()), ('rf', RandomForestClassifier())])
SimpleImputer()
RandomForestClassifier()