maitelizarraga/rea-xgboost

Model Trained Using AutoTrain

• Problem type: Tabular regression

Validation Metrics

• r2: 0.3033866286277771
• mse: 1798692953.5327067
• mae: 31881.1203125
• rmse: 42411.00038354091
• rmsle: 0.20291934835125106
• loss: 42411.00038354091

Best Params

• learning_rate: 0.10040353638173113
• reg_lambda: 0.006827780870976135
• reg_alpha: 0.006625264866744126
• subsample: 0.25905346245387173
• colsample_bytree: 0.2072843639904269
• max_depth: 4
• early_stopping_rounds: 122
• 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