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metadata
tags:
  - autotrain
  - tabular
  - regression
  - tabular-regression
datasets:
  - autotrain-uljkp-sdhgs/autotrain-data

Model Trained Using AutoTrain

  • Problem type: Tabular regression

Validation Metrics

  • r2: 0.9900762497798218
  • mse: 10317.805777253338
  • mae: 74.54517527770996
  • rmse: 101.57660053995377
  • rmsle: 0.042811727450114016
  • loss: 101.57660053995377

Best Params

  • learning_rate: 0.016479102091350954
  • reg_lambda: 0.3449233788687026
  • reg_alpha: 3.244557908377455e-07
  • subsample: 0.5379679408548034
  • colsample_bytree: 0.9050706969365716
  • max_depth: 4
  • early_stopping_rounds: 293
  • n_estimators: 7000
  • 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