
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624

## Validation Metrics

- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9

## 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-iris-train", "scikit-learn/iris"], "co2_eq_emissions": 0.0006300767567816624}
transformers joblib logistic_regression autotrain tabular classification tabular-classification dataset:abhishek/autotrain-data-iris-train dataset:scikit-learn/iris co2_eq_emissions endpoints_compatible has_space region:us
TAGS
#transformers #joblib #logistic_regression #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624

## Validation Metrics

- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9

## Usage


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624 ## Validation Metrics

- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9 ## Usage
TAGS
#transformers #joblib #logistic_regression #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624 ## Validation Metrics

- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9 ## Usage
passage: TAGS
#transformers #joblib #logistic_regression #autotrain #tabular #classification #tabular-classification #dataset-abhishek/autotrain-data-iris-train #dataset-scikit-learn/iris #co2_eq_emissions #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 9705273
- CO2 Emissions (in grams): 0.0006300767567816624## Validation Metrics

- Loss: 0.15987505325856152
- Accuracy: 0.9
- Macro F1: 0.899749373433584
- Micro F1: 0.9
- Weighted F1: 0.8997493734335841
- Macro Precision: 0.9023569023569024
- Micro Precision: 0.9
- Weighted Precision: 0.9023569023569025
- Macro Recall: 0.9
- Micro Recall: 0.9
- Weighted Recall: 0.9## Usage