---
tags:
- tabular
- classification
- tabular-classification
- google-ads
widget:
  structuredData:
    keyword:
    - garner
    - chevy
    - location
    class:
    - brand
    - brand
    - geo
datasets:
- adgrowr/autotrain-data-negative-keywords-classifier
co2_eq_emissions:
  emissions: 1.2831572182351383
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 61622134846
- CO2 Emissions (in grams): 1.2832

## Validation Metrics

- Loss: 0.883
- Accuracy: 0.583
- Macro F1: 0.184
- Micro F1: 0.583
- Weighted F1: 0.429
- Macro Precision: 0.146
- Micro Precision: 0.583
- Weighted Precision: 0.340
- Macro Recall: 0.250
- Micro Recall: 0.583
- Weighted Recall: 0.583

## 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)

```