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Model Trained Using AutoTrain

  • Problem type: Tabular regression

Validation Metrics

  • r2: 0.33913886288678097
  • mse: 0.13878083879377598
  • mae: 0.2991213083267212
  • rmse: 0.37253300363025016
  • rmsle: 0.15062628429771513
  • loss: 0.37253300363025016

Best Params

  • learning_rate: 0.017092100292696658
  • reg_lambda: 2.08790995148619e-05
  • reg_alpha: 5.763917500537152e-06
  • subsample: 0.38707603768089427
  • colsample_bytree: 0.547982260603956
  • max_depth: 1
  • early_stopping_rounds: 102
  • 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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