| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - gvozdev/autotrain-data-autotrain-ratings | |
| # Model Trained Using AutoTrain | |
| - Problem type: Tabular regression | |
| ## Validation Metrics | |
| - r2: 0.004852553257630565 | |
| - mse: 1.704782407585897 | |
| - mae: 1.0301575550030646 | |
| - rmse: 1.3056731626199174 | |
| - rmsle: 0.1919556417083651 | |
| - loss: 1.3056731626199174 | |
| ## Best Params | |
| - learning_rate: 0.16113054215755473 | |
| - reg_lambda: 3.3566663737449463e-06 | |
| - reg_alpha: 1.999845686956423e-05 | |
| - subsample: 0.3521158025399591 | |
| - colsample_bytree: 0.1661721364825762 | |
| - max_depth: 2 | |
| - early_stopping_rounds: 172 | |
| - 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 | |
| ``` | |