atheno3/autotrain-e7z27-ikr13

Model Trained Using AutoTrain

• Problem type: Tabular classification

Validation Metrics

• auc: 0.5845690136815017
• logloss: 0.3365109999879715
• f1: 0.9423455684870188
• accuracy: 0.8926666666666667
• precision: 0.8949158306410474
• recall: 0.9950841368878806
• loss: 0.3365109999879715

Best Params

• learning_rate: 0.024903253168743332
• reg_lambda: 7.491240117115577e-05
• reg_alpha: 4.545201602072796e-08
• subsample: 0.5376101864226687
• colsample_bytree: 0.7150042050371038
• max_depth: 2
• early_stopping_rounds: 368
• n_estimators: 7000
• eval_metric: logloss

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