| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - rea-svm/autotrain-data | |
| # Model Trained Using AutoTrain | |
| - Problem type: Tabular regression | |
| ## Validation Metrics | |
| - r2: -13.723120886842382 | |
| - mse: 38015883737.055504 | |
| - mae: 174448.73032973852 | |
| - rmse: 194976.62356563544 | |
| - rmsle: 2.6073960566969823 | |
| - loss: 194976.62356563544 | |
| ## Best Params | |
| - C: 60.27729201980406 | |
| - fit_intercept: False | |
| - loss: epsilon_insensitive | |
| - epsilon: 0.00020827552565594136 | |
| - max_iter: 9903 | |
| ## 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 | |
| ``` | |