Instructions to use molkab/dashboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use molkab/dashboard with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://molkab/dashboard") - Notebooks
- Google Colab
- Kaggle
| ,pct_detection_%,valid_metier_%,instabilite,pval_stabilite,score_stabilite,overlap_ensemble_%,type_dominant,score_composite | |
| Isolation Forest,6.44,27.6,0.05,0.2752,100.0,69.6,t4_%,70.65 | |
| One-Class SVM,6.64,24.1,0.04,0.2997,100.0,73.9,t4_%,70.28 | |
| GMM,6.23,24.0,0.01,0.8908,100.0,50.9,t4_%,64.52 | |
| Elliptic Envelope,6.65,23.8,0.16,0.0001,98.4,49.3,t4_%,63.62 | |
| DBSCAN,2.97,36.3,0.0,0.9712,100.0,43.9,t4_%,59.96 | |
| LOF,6.99,12.7,0.36,0.0,96.4,17.4,t4_%,51.15 | |
| Autoencoder,6.04,8.0,0.14,0.0006,98.6,13.4,t4_%,49.62 | |