Skreeauk/dark-gbf-xgboost2

Model Trained Using AutoTrain

• Problem type: Tabular classification

Validation Metrics

• logloss: 0.08323427141158712
• accuracy: 0.98
• mlogloss: 0.08323427141158712
• f1_macro: 0.8266666666666665
• f1_micro: 0.98
• f1_weighted: 0.9793333333333333
• precision_macro: 0.8666666666666666
• precision_micro: 0.98
• precision_weighted: 0.9833333333333333
• recall_macro: 0.8333333333333333
• recall_micro: 0.98
• recall_weighted: 0.98
• loss: 0.08323427141158712

Best Params

• learning_rate: 0.16433034910560887
• reg_lambda: 3.7914578973926436
• reg_alpha: 2.806649620056883e-07
• subsample: 0.7396301555452317
• colsample_bytree: 0.9137471530067593
• max_depth: 6
• early_stopping_rounds: 383
• n_estimators: 15000
• eval_metric: mlogloss

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