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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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- BrianDsouzaAI/autotrain-data-tab-multi |
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widget: |
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structuredData: |
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Planned_Stories: |
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- 10 |
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- 20 |
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- 30 |
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Delivered_Stories: |
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- 11 |
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- 12 |
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- 13 |
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co2_eq_emissions: |
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emissions: 2.067391665478424 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-label Classification |
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- Model ID: 92337144714 |
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- CO2 Emissions (in grams): 2.0674 |
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## Validation Metrics |
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- Loss: 3.634 |
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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 numpy as np |
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import pandas as pd |
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models = 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 = [] |
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for model_ in models: |
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predictions_ = model_.predict(data) # or model.predict_proba(data)[:, 1] |
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predictions.append(predictions_) |
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predictions = np.column_stack(predictions) |
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``` |