MahsaShirazian/autotrain-eno5u-hy49n

tags:

• —autotrain
• —tabular
• —regression
• —tabular-regression datasets:
• —autotrain-eno5u-hy49n/autotrain-data ---

Model Trained Using AutoTrain

• —Problem type: Tabular regression

Validation Metrics

• —r2: 0.9988073651686958
• —mse: 337854.8880689932
• —mae: 398.9371570016889
• —rmse: 581.2528606974706
• —rmsle: 0.01184006241771929
• —loss: 581.2528606974706

Best Params

• —learning_rate: 0.09168110099890295
• —reg_lambda: 0.11592417839619104
• —reg_alpha: 0.0010410090431649107
• —subsample: 0.357364833100802
• —colsample_bytree: 0.9155936367985213
• —max_depth: 7
• —earlystoppingrounds: 158
• —n_estimators: 15000
• —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