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Support Vector Classifier model trained on JeVeuxAider dataset. As input, the model takes text embeddings encoded with camembert-base (768 tokens)| Hyperparameter | Value |
|---|---|
| memory | |
| steps | [('columntransformer', ColumnTransformer(transformers=[('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8', 'avg_9', 'avg_10', ... 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764', 'max_765', 'max_766', 'max_767', 'max_768'], dtype='object', length=2304))], verbose_feature_names_out=False)), ('svc', SVC(probability=True, random_state=42))] |
| verbose | False |
| columntransformer | ColumnTransformer(transformers=[('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8', 'avg_9', 'avg_10', ... 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764', 'max_765', 'max_766', 'max_767', 'max_768'], dtype='object', length=2304))], verbose_feature_names_out=False) |
| svc | SVC(probability=True, random_state=42) |
| columntransformer__n_jobs | |
| columntransformer__remainder | drop |
| columntransformer__sparse_threshold | 0.3 |
| columntransformer__transformer_weights | |
| columntransformer__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]), Index(['avg_1', 'avg_2', 'avg_3', 'avg_4', 'avg_5', 'avg_6', 'avg_7', 'avg_8', 'avg_9', 'avg_10', ... 'max_759', 'max_760', 'max_761', 'max_762', 'max_763', 'max_764', 'max_765', 'max_766', 'max_767', 'max_768'], dtype='object', length=2304))] |
| columntransformer__verbose | False |
| columntransformer__verbose_feature_names_out | False |
| columntransformer__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))]) |
| columntransformer__num__memory | |
| columntransformer__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('pca', PCA(n_components=563))] |
| columntransformer__num__verbose | False |
| columntransformer__num__imputer | SimpleImputer(strategy='median') |
| columntransformer__num__scaler | StandardScaler() |
| columntransformer__num__pca | PCA(n_components=563) |
| columntransformer__num__imputer__add_indicator | False |
| columntransformer__num__imputer__copy | True |
| columntransformer__num__imputer__fill_value | |
| columntransformer__num__imputer__keep_empty_features | False |
| columntransformer__num__imputer__missing_values | nan |
| columntransformer__num__imputer__strategy | median |
| columntransformer__num__imputer__verbose | deprecated |
| columntransformer__num__scaler__copy | True |
| columntransformer__num__scaler__with_mean | True |
| columntransformer__num__scaler__with_std | True |
| columntransformer__num__pca__copy | True |
| columntransformer__num__pca__iterated_power | auto |
| columntransformer__num__pca__n_components | 563 |
| columntransformer__num__pca__n_oversamples | 10 |
| columntransformer__num__pca__power_iteration_normalizer | auto |
| columntransformer__num__pca__random_state | |
| columntransformer__num__pca__svd_solver | auto |
| columntransformer__num__pca__tol | 0.0 |
| columntransformer__num__pca__whiten | False |
| svc__C | 1.0 |
| svc__break_ties | False |
| svc__cache_size | 200 |
| svc__class_weight | |
| svc__coef0 | 0.0 |
| svc__decision_function_shape | ovr |
| svc__degree | 3 |
| svc__gamma | scale |
| svc__kernel | rbf |
| svc__max_iter | -1 |
| svc__probability | True |
| svc__random_state | 42 |
| svc__shrinking | True |
| svc__tol | 0.001 |
| svc__verbose | False |
| Metric | Value |
|---|---|
| accuracy | 0.985849 |
| f1 score | 0.985849 |
@inproceedings{...,year={2023}}