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--- |
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tags: |
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- tabular |
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- classification |
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- tabular-classification |
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- google-ads |
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widget: |
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structuredData: |
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keyword: |
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- garner |
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- chevy |
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- location |
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class: |
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- brand |
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- brand |
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- geo |
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datasets: |
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- adgrowr/autotrain-data-negative-keywords-classifier |
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co2_eq_emissions: |
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emissions: 1.2831572182351383 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: 61622134846 |
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- CO2 Emissions (in grams): 1.2832 |
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## Validation Metrics |
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- Loss: 0.883 |
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- Accuracy: 0.583 |
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- Macro F1: 0.184 |
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- Micro F1: 0.583 |
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- Weighted F1: 0.429 |
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- Macro Precision: 0.146 |
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- Micro Precision: 0.583 |
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- Weighted Precision: 0.340 |
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- Macro Recall: 0.250 |
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- Micro Recall: 0.583 |
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- Weighted Recall: 0.583 |
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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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data.columns = ["feat_" + str(col) for col in data.columns] |
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predictions = model.predict(data) # or model.predict_proba(data) |
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``` |
