
# Model Trained Using AutoTrain

- Problem type: Multi-label Classification
- Model ID: 92337144714
- CO2 Emissions (in grams): 2.0674

## Validation Metrics

- Loss: 3.634

## Usage

```python
import json
import joblib

import numpy as np
import pandas as pd

models = joblib.load('model.joblib')
config = json.load(open('config.json'))

features = config['features']

# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]

predictions = []
for model_ in models:
    predictions_ = model_.predict(data)  # or model.predict_proba(data)[:, 1]
    predictions.append(predictions_)

predictions = np.column_stack(predictions)

```
{"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["BrianDsouzaAI/autotrain-data-tab-multi"], "widget": {"structuredData": {"Planned_Stories": [10, 20, 30], "Delivered_Stories": [11, 12, 13]}}, "co2_eq_emissions": {"emissions": 2.067391665478424}}
transformers joblib xgboost autotrain tabular classification tabular-classification dataset:BrianDsouzaAI/autotrain-data-tab-multi co2_eq_emissions endpoints_compatible region:us