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Model Trained Using AutoTrain

  • Problem type: Tabular classification

Validation Metrics

  • logloss: 0.08323427141158712
  • accuracy: 0.98
  • mlogloss: 0.08323427141158712
  • f1_macro: 0.8266666666666665
  • f1_micro: 0.98
  • f1_weighted: 0.9793333333333333
  • precision_macro: 0.8666666666666666
  • precision_micro: 0.98
  • precision_weighted: 0.9833333333333333
  • recall_macro: 0.8333333333333333
  • recall_micro: 0.98
  • recall_weighted: 0.98
  • loss: 0.08323427141158712

Best Params

  • learning_rate: 0.16433034910560887
  • reg_lambda: 3.7914578973926436
  • reg_alpha: 2.806649620056883e-07
  • subsample: 0.7396301555452317
  • colsample_bytree: 0.9137471530067593
  • max_depth: 6
  • early_stopping_rounds: 383
  • n_estimators: 15000
  • eval_metric: mlogloss

Usage

import json
import joblib
import pandas as pd

model = joblib.load('model.joblib')
config = json.load(open('config.json'))

features = config['features']

# data = pd.read_csv("data.csv")
data = data[features]

predictions = model.predict(data)  # or model.predict_proba(data)

# predictions can be converted to original labels using label_encoders.pkl
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