
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49402119333
- CO2 Emissions (in grams): 8.1104

## Validation Metrics

- Loss: 0.246
- Accuracy: 0.889
- Macro F1: 0.867
- Micro F1: 0.889
- Weighted F1: 0.885
- Macro Precision: 0.886
- Micro Precision: 0.889
- Weighted Precision: 0.885
- Macro Recall: 0.851
- Micro Recall: 0.889
- Weighted Recall: 0.889

## 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": ["Xinhhd/autotrain-data-zhongxin-contest"], "co2_eq_emissions": {"emissions": 8.110415102615649}}
transformers joblib xgboost autotrain tabular classification tabular-classification dataset:Xinhhd/autotrain-data-zhongxin-contest co2_eq_emissions endpoints_compatible has_space region:us
TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-Xinhhd/autotrain-data-zhongxin-contest #co2_eq_emissions #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49402119333
- CO2 Emissions (in grams): 8.1104

## Validation Metrics

- Loss: 0.246
- Accuracy: 0.889
- Macro F1: 0.867
- Micro F1: 0.889
- Weighted F1: 0.885
- Macro Precision: 0.886
- Micro Precision: 0.889
- Weighted Precision: 0.885
- Macro Recall: 0.851
- Micro Recall: 0.889
- Weighted Recall: 0.889

## Usage


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49402119333
- CO2 Emissions (in grams): 8.1104 ## Validation Metrics

- Loss: 0.246
- Accuracy: 0.889
- Macro F1: 0.867
- Micro F1: 0.889
- Weighted F1: 0.885
- Macro Precision: 0.886
- Micro Precision: 0.889
- Weighted Precision: 0.885
- Macro Recall: 0.851
- Micro Recall: 0.889
- Weighted Recall: 0.889 ## Usage
TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-Xinhhd/autotrain-data-zhongxin-contest #co2_eq_emissions #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49402119333
- CO2 Emissions (in grams): 8.1104 ## Validation Metrics

- Loss: 0.246
- Accuracy: 0.889
- Macro F1: 0.867
- Micro F1: 0.889
- Weighted F1: 0.885
- Macro Precision: 0.886
- Micro Precision: 0.889
- Weighted Precision: 0.885
- Macro Recall: 0.851
- Micro Recall: 0.889
- Weighted Recall: 0.889 ## Usage
TAGS
#transformers #joblib #xgboost #autotrain #tabular #classification #tabular-classification #dataset-Xinhhd/autotrain-data-zhongxin-contest #co2_eq_emissions #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 49402119333
- CO2 Emissions (in grams): 8.1104## Validation Metrics

- Loss: 0.246
- Accuracy: 0.889
- Macro F1: 0.867
- Micro F1: 0.889
- Weighted F1: 0.885
- Macro Precision: 0.886
- Micro Precision: 0.889
- Weighted Precision: 0.885
- Macro Recall: 0.851
- Micro Recall: 0.889
- Weighted Recall: 0.889## Usage