|
--- |
|
tags: |
|
- autotrain |
|
- tabular |
|
- classification |
|
- tabular-classification |
|
datasets: |
|
- fyhao/autotrain-data-sentiment-analysis |
|
co2_eq_emissions: |
|
emissions: 0.0803280731181239 |
|
--- |
|
|
|
# Model Trained Using AutoTrain |
|
|
|
- Problem type: Multi-class Classification |
|
- Model ID: 2435575634 |
|
- CO2 Emissions (in grams): 0.0803 |
|
|
|
## Validation Metrics |
|
|
|
- Loss: 0.186 |
|
- Accuracy: 0.873 |
|
- Macro F1: 0.870 |
|
- Micro F1: 0.873 |
|
- Weighted F1: 0.868 |
|
- Macro Precision: 0.938 |
|
- Micro Precision: 0.873 |
|
- Weighted Precision: 0.896 |
|
- Macro Recall: 0.833 |
|
- Micro Recall: 0.873 |
|
- Weighted Recall: 0.873 |
|
|
|
## 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) |
|
|
|
``` |
