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test-mcp
X-13A/test-mcp
test-mcp is a tabular classification model from X-13A. Use it for the tabular classification task on the model card, and read the license before you ship it in a product.
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Public
Updated Sep 13, 2025
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4.1 KB
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.joblib4.1 KB · 56%
At a glance
- Task
- Tabular Classification
- Access
- Public
- Created
- Sep 13, 2025
- Updated
- Sep 13, 2025
- SHA
- 20a47d4f
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Tabular classification
Validation Metrics
- logloss: 0.28027260106321344
- accuracy: 0.8666666666666667
- mlogloss: 0.28027260106321344
- f1_macro: 0.8611111111111112
- f1_micro: 0.8666666666666667
- f1_weighted: 0.8611111111111112
- precision_macro: 0.9047619047619048
- precision_micro: 0.8666666666666667
- precision_weighted: 0.9047619047619048
- recall_macro: 0.8666666666666667
- recall_micro: 0.8666666666666667
- recall_weighted: 0.8666666666666667
- loss: 0.28027260106321344
Best Params
- C: 360.26071213844074
- fit_intercept: False
- solver: saga
- penalty: l2
Usage
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]
predictions = model.predict(data) # or model.predict_proba(data)
# predictions can be converted to original labels using label_encoders.pkl
- Task
- Tabular Classification
- Created
- Sep 13, 2025
- Updated
- Sep 13, 2025