
# Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 40376105012
- CO2 Emissions (in grams): 27.4692

## Validation Metrics

- Loss: 0.211
- R2: 0.339
- MSE: 0.045
- MAE: 0.104
- RMSLE: 0.145

## 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", "regression", "tabular-regression"], "datasets": ["bibekbehera/autotrain-data-numeric_prediction"], "co2_eq_emissions": {"emissions": 27.469239802379224}}
transformers joblib extra_trees autotrain tabular regression tabular-regression dataset:bibekbehera/autotrain-data-numeric_prediction co2_eq_emissions endpoints_compatible has_space region:us
TAGS
#transformers #joblib #extra_trees #autotrain #tabular #regression #tabular-regression #dataset-bibekbehera/autotrain-data-numeric_prediction #co2_eq_emissions #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 40376105012
- CO2 Emissions (in grams): 27.4692

## Validation Metrics

- Loss: 0.211
- R2: 0.339
- MSE: 0.045
- MAE: 0.104
- RMSLE: 0.145

## Usage


# Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 40376105012
- CO2 Emissions (in grams): 27.4692 ## Validation Metrics

- Loss: 0.211
- R2: 0.339
- MSE: 0.045
- MAE: 0.104
- RMSLE: 0.145 ## Usage
TAGS
#transformers #joblib #extra_trees #autotrain #tabular #regression #tabular-regression #dataset-bibekbehera/autotrain-data-numeric_prediction #co2_eq_emissions #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Single Column Regression
- Model ID: 40376105012
- CO2 Emissions (in grams): 27.4692 ## Validation Metrics

- Loss: 0.211
- R2: 0.339
- MSE: 0.045
- MAE: 0.104
- RMSLE: 0.145 ## Usage