| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - rea-xgboost/autotrain-data | |
| # 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 | |
| ```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 | |
| ``` | |