| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - rea-knn/autotrain-data | |
| # Model Trained Using AutoTrain | |
| - Problem type: Tabular regression | |
| ## Validation Metrics | |
| - r2: 0.4161997019836754 | |
| - mse: 1507403520.3284101 | |
| - mae: 29120.68408236499 | |
| - rmse: 38825.29485178973 | |
| - rmsle: 0.18675257705362744 | |
| - loss: 38825.29485178973 | |
| ## Best Params | |
| - n_neighbors: 3 | |
| - weights: distance | |
| - algorithm: ball_tree | |
| - leaf_size: 77 | |
| - p: 2 | |
| - metric: manhattan | |
| ## 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 | |
| ``` | |