jul-fls/autotrain-fbyte-l9vfz

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