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metadata
tags:
  - autotrain
  - tabular
  - regression
  - tabular-regression
datasets:
  - nicoler229/autotrain-data-renp-vcyx-5hff

Model Trained Using AutoTrain

  • Problem type: Tabular regression

Validation Metrics

  • r2: 0.8987710422047952
  • mse: 15.386801584871137
  • mae: 3.1008129119873047
  • rmse: 3.9226013798079378
  • rmsle: 0.049014949862444
  • loss: 3.9226013798079378

Best Params

  • learning_rate: 0.09858308825036341
  • reg_lambda: 1.7244892825164977e-06
  • reg_alpha: 0.004880162297132929
  • subsample: 0.5918267532876357
  • colsample_bytree: 0.6228647593929555
  • max_depth: 8
  • early_stopping_rounds: 440
  • 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