
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977

## Validation Metrics

- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.7191166321601105

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

predictions = model.predict(data)  # or model.predict_proba(data)

```
{"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["abhishek/autotrain-data-adult-train", "scikit-learn/adult-census-income"], "co2_eq_emissions": 0.12693590577861977}
transformers joblib xgboost autotrain tabular classification tabular-classification dataset:abhishek/autotrain-data-adult-train dataset:scikit-learn/adult-census-income co2_eq_emissions endpoints_compatible region:us
TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-adult-train #dataset-scikit-learn/adult-census-income #co2_eq_emissions #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977

## Validation Metrics

- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.7191166321601105

## Usage


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977 ## Validation Metrics

- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.7191166321601105 ## Usage
TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-adult-train #dataset-scikit-learn/adult-census-income #co2_eq_emissions #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977 ## Validation Metrics

- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.7191166321601105 ## Usage
passage: TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-adult-train #dataset-scikit-learn/adult-census-income #co2_eq_emissions #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 9725286
- CO2 Emissions (in grams): 0.12693590577861977## Validation Metrics

- Loss: 0.26716182056213406
- Accuracy: 0.8750191923844618
- Precision: 0.7840481565086531
- Recall: 0.6641172721478649
- AUC: 0.9345322809861784
- F1: 0.7191166321601105## Usage