Instructions to use ikhbarikhbar/titanic-survival-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ikhbarikhbar/titanic-survival-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("ikhbarikhbar/titanic-survival-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Titanic Survival Classifier
Binary classification model predicting passenger survival on the Titanic.
Dataset
- Source: julien-c/titanic-survival
- Task: Binary classification (
Survived)
Model
- Type: GradientBoosting
- Framework: scikit-learn
Performance
| Metric | Value |
|---|---|
| CV AUC (mean ± std) | 0.8792 ± 0.0214 |
| Test Accuracy | 0.7809 |
| Test Precision | 0.7419 |
| Test Recall | 0.6667 |
| Test F1 | 0.7023 |
| Test AUC | 0.8348 |
Usage
import skops.io as sio
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="ikhbarikhbar/titanic-survival-classifier", filename="model.skops")
model = sio.load(path, trusted=True)
Features
Numeric: Pclass, Age, Siblings/Spouses Aboard, Parents/Children Aboard, Fare, FamilySize, IsAlone, FarePerPerson
Categorical: SexStr, PclassCat, Title, AgeGroup
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