---

tags:

• autotrain
• tabular
• regression
• tabular-regression datasets:
• will-clarke/autotrain-data-km3p-5cou-dikk

---

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