README.md

metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_format: pickle
model_file: rf_model.pkl
widget:
  structuredData:
    cons_12m:
      - 22353
      - 18097
      - 1893
    cons_last_month:
      - 300
      - 0
      - 0
    contract_length:
      - 2574
      - 2243
      - 2393
    forecast_cons_12m:
      - 1376.530029296875
      - 1810.1199951171875
      - 284.42999267578125
    forecast_cons_year:
      - 0
      - 0
      - 0
    forecast_discount_energy:
      - 0
      - 0
      - 0
    forecast_meter_rent_12m:
      - 0
      - 126.66000366210938
      - 19.809999465942383
    forecast_price_pow_off_peak:
      - 44.311378479003906
      - 40.6067008972168
      - 44.311378479003906
    has_gas_t:
      - 0
      - 0
      - 0
    imp_cons:
      - 0
      - 0
      - 0
    margin_gross_pow_ele:
      - 29.5
      - 27
      - 9.399999618530273
    nb_prod_act:
      - 1
      - 1
      - 1
    num_years_antig:
      - 6
      - 6
      - 6
    pow_max:
      - 14.300000190734863
      - 18
      - 12.5
    price_diff_energy_peak_offpeak:
      - -0.1458740234375
      - -0.016845703125
      - -0.145751953125

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
bootstrap	True
ccp_alpha	0.0
class_weight
criterion	gini
max_depth
max_features	sqrt
max_leaf_nodes
max_samples
min_impurity_decrease	0.0
min_samples_leaf	1
min_samples_split	2
min_weight_fraction_leaf	0.0
n_estimators	25
n_jobs	-1
oob_score	False
random_state	1
verbose	0
warm_start	False

Model Plot

The model plot is below.

RandomForestClassifier(n_estimators=25, n_jobs=-1, random_state=1)

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Evaluation Results

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

Metric	Value
accuracy	0.988057
f1 score	0.988057

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={2023}}

get_started_code

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

model_card_authors

Marvin Lomo

limitations

This model is not ready to be used in production.

model_description

This is a RandomForrestClassifier model trained on SME Churn Dataset.

eval_method

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

confusion_matrix