| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - will-clarke/autotrain-data-km3p-5cou-dikk | |
| # Model Trained Using AutoTrain | |
| - Problem type: Tabular regression | |
| ## Validation Metrics | |
| - r2: -0.008598428559009497 | |
| - mse: 598.976166598342 | |
| - mae: 9.062458591043514 | |
| - rmse: 24.473989593001424 | |
| - rmsle: 1.2592486785782957 | |
| - loss: 24.473989593001424 | |
| ## Best Params | |
| - learning_rate: 0.05243299592316927 | |
| - reg_lambda: 6.717966298706072e-08 | |
| - reg_alpha: 1.6032915106085746e-08 | |
| - subsample: 0.5114836334096384 | |
| - colsample_bytree: 0.42603286105240046 | |
| - max_depth: 1 | |
| - early_stopping_rounds: 455 | |
| - n_estimators: 20000 | |
| - eval_metric: rmse | |
| ## Usage | |
| ```python | |
| 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 | |
| ``` | |