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--- |
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tags: |
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- autotrain |
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- tabular |
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- classification |
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- tabular-classification |
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datasets: |
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- dark-gbf-xgboost2/autotrain-data |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Tabular classification |
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## Validation Metrics |
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- logloss: 0.08323427141158712 |
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- accuracy: 0.98 |
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- mlogloss: 0.08323427141158712 |
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- f1_macro: 0.8266666666666665 |
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- f1_micro: 0.98 |
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- f1_weighted: 0.9793333333333333 |
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- precision_macro: 0.8666666666666666 |
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- precision_micro: 0.98 |
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- precision_weighted: 0.9833333333333333 |
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- recall_macro: 0.8333333333333333 |
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- recall_micro: 0.98 |
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- recall_weighted: 0.98 |
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- loss: 0.08323427141158712 |
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## Best Params |
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- learning_rate: 0.16433034910560887 |
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- reg_lambda: 3.7914578973926436 |
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- reg_alpha: 2.806649620056883e-07 |
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- subsample: 0.7396301555452317 |
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- colsample_bytree: 0.9137471530067593 |
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- max_depth: 6 |
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- early_stopping_rounds: 383 |
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- n_estimators: 15000 |
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- eval_metric: mlogloss |
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## Usage |
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```python |
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import json |
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import joblib |
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import pandas as pd |
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model = joblib.load('model.joblib') |
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config = json.load(open('config.json')) |
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features = config['features'] |
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# data = pd.read_csv("data.csv") |
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data = data[features] |
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predictions = model.predict(data) # or model.predict_proba(data) |
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# predictions can be converted to original labels using label_encoders.pkl |
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``` |
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