Tabular Classification
Keras
Scikit-learn
English
tensorflow
random-forest
cnn
clustering
nlp
computer-vision
recommendation-system
time-series
streamlit
Instructions to use OKTAYBBS/DataScientst-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OKTAYBBS/DataScientst-models with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://OKTAYBBS/DataScientst-models") - Scikit-learn
How to use OKTAYBBS/DataScientst-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("OKTAYBBS/DataScientst-models", "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
Delete metrics_summary.csv with huggingface_hub
Browse files- metrics_summary.csv +0 -26
metrics_summary.csv
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Proje,Tip,Skor,Skor Adı
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gold,Regresyon,0.9904,R²
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student,Regresyon,0.8489,R²
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uber,Regresyon,0.7775,R²
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mobile,Sınıflandırma,0.8125,Accuracy
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wine,Sınıflandırma,0.675,Accuracy
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churn,Sınıflandırma,0.7889,Accuracy
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nba,Kümeleme,0.452,Silhouette
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creditcard,Kümeleme,0.5309,Silhouette
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spotify,Kümeleme,0.3269,Silhouette
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mask,Sınıflandırma,0.825,Accuracy
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sms,Sınıflandırma,0.9803,Accuracy
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imdb_sentiment,Sınıflandırma,0.873,Accuracy
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fake_news,Sınıflandırma,0.9756,Accuracy
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movie_rec,Metriksiz,,-
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book_rec,Metriksiz,,-
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song_rec,Metriksiz,,-
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stock,Regresyon,0.975,R²
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weather,Regresyon,0.912,R²
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walmart,Regresyon,0.7669,R²
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social_media_viz,Metriksiz,,-
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co2_viz,Metriksiz,,-
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ecommerce_viz,Metriksiz,,-
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pneumonia,Derin Öğrenme,0.925,Val_Accuracy
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face_emotion,Derin Öğrenme,0.6542,Val_Accuracy
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text_gen,Metriksiz,,-
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