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Model Trained Using AutoTrain

  • Problem type: Tabular regression

Validation Metrics

  • r2: -0.008598428559009497
  • mse: 598.976166598342
  • mae: 9.062458591043514
  • rmse: 24.473989593001424
  • rmsle: 1.2592486785782957
  • loss: 24.473989593001424

Best Params

  • learning_rate: 0.05243299592316927
  • reg_lambda: 6.717966298706072e-08
  • reg_alpha: 1.6032915106085746e-08
  • subsample: 0.5114836334096384
  • colsample_bytree: 0.42603286105240046
  • max_depth: 1
  • early_stopping_rounds: 455
  • n_estimators: 20000
  • eval_metric: rmse

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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