diff --git a/mkdocs/docs/development/dev.md b/mkdocs/docs/development/dev.md
index ea2dd55..0fed58d 100644
--- a/mkdocs/docs/development/dev.md
+++ b/mkdocs/docs/development/dev.md
@@ -254,7 +254,7 @@ The semantic release configuration is defined in `pyproject.toml`:
 [tool.semantic_release]
 version_toml = ["pyproject.toml:project.version"]
 build_command = "uv build"
-upload_to_release = true
+upload_to_vcs_release = true
 
 [tool.semantic_release.changelog]
 mode = "update"
@@ -280,14 +280,11 @@ Releases are automatically triggered when commits are pushed to the `main` branc
 While releases are automated, you can manually trigger a release:
 
 ```bash
-# Dry run to see what would happen
-uv run semantic-release version --no-push
+# Print the next version without changing files
+uv run semantic-release version --print
 
 # Generate changelog only
 uv run semantic-release changelog
-
-# Print current version
-uv run semantic-release version --print
 ```
 
 ### Best Practices
diff --git a/mkdocs/docs/feature_generation.md b/mkdocs/docs/feature_generation.md
index 2e9c565..4cd5de6 100644
--- a/mkdocs/docs/feature_generation.md
+++ b/mkdocs/docs/feature_generation.md
@@ -31,12 +31,12 @@ for k, v in Descriptors.descList:
 
     #### Binary vs. Count Fingerprints
 
-    By default, fingerprints are generated as **binary** vectors — each bit is either `0` (substructure absent) or `1` (substructure present). You can switch to **count-based** fingerprints by setting `use_counts=True`, which records ***how many times*** each substructure occurs in the molecule. Count fingerprints can improve model performance when the frequency of substructures is informative.
+    By default, `FingerprintsGeneric` and `MorganFingerprints` generate **count-based** vectors, which record ***how many times*** each substructure occurs in a molecule. Count fingerprints can improve model performance when substructure frequency is informative. Set `use_counts=False` to generate a binary vector, where each bit is either `0` (substructure absent) or `1` (substructure present).
 
     | Mode | Parameter | Output values | Use case |
     |------|-----------|---------------|----------|
-    | Binary (default) | `use_counts=False` | 0 or 1 | General-purpose fingerprinting |
-    | Count | `use_counts=True` | 0, 1, 2, … | When substructure frequency matters |
+    | Count (default) | `use_counts=True` | 0, 1, 2, ... | When substructure frequency matters |
+    | Binary | `use_counts=False` | 0 or 1 | When only substructure presence matters |
 
     The `use_counts` parameter is available on both `FingerprintsGeneric` and the `MorganFingerprints` convenience class:
 
@@ -46,17 +46,19 @@ from mother.feature_generation import FingerprintsGeneric, MorganFingerprints
 
 molecule_objects = [Chem.MolFromSmiles(smi) for smi in ["CCO", "CCN", "c1ccccc1"]]
 
-# Count-based Morgan fingerprints via the convenience class
-morgan_counts = MorganFingerprints(radius=2, fpSize=1024, use_counts=True)
+# Count-based Morgan fingerprints are the default
+morgan_counts = MorganFingerprints(radius=2, fpSize=2048)
 features = morgan_counts.fit_transform(molecule_objects)
 
 # Count-based fingerprints via the generic class (works with any supported fp_type)
 fp_counts = FingerprintsGeneric(
     fp_type="AtomPairFP",
     parameters={"fpSize": 2048},
-    use_counts=True,
 )
 features = fp_counts.fit_transform(molecule_objects)
+
+# Opt in to binary Morgan fingerprints
+morgan_binary = MorganFingerprints(radius=2, fpSize=2048, use_counts=False)
 ```
 
     !!! note
@@ -86,10 +88,12 @@ from mother.feature_generation import ChemicalDescriptors, MorganFingerprints
 
 molecule_objects = [Chem.MolFromSmiles(smi) for smi in ["CCO", "CCN", "c1ccccc1"]]
 
-feature_generator = FeatureUnion([
-    ("descriptors", ChemicalDescriptors(descriptor_list=["MolWt", "MolLogP"])),
-    ("morgan_fp", MorganFingerprints(radius=2, fpSize=1024)),
-])
+feature_generator = FeatureUnion(
+    [
+        ("descriptors", ChemicalDescriptors(descriptor_list=["MolWt", "MolLogP"])),
+        ("morgan_fp", MorganFingerprints(radius=2, fpSize=2048)),
+    ]
+)
 features = feature_generator.fit_transform(molecule_objects)
 ```
 
diff --git a/src/mother/feature_generation/config.py b/src/mother/feature_generation/config.py
index 416dbdf..0891afd 100644
--- a/src/mother/feature_generation/config.py
+++ b/src/mother/feature_generation/config.py
@@ -139,7 +139,7 @@ class FeatureGenerationConfig(BaseModel):
     fingerprints: List[Dict[str, Any]] = Field(default=[], description="List of fingerprint generator settings")
     maccs: bool = Field(default=False, description="Flag if maccs fingerprints should be generated")
     chemical_descriptors: Optional[ChemicalDescriptorsParams] = Field(default=None)
-    use_counts: bool = Field(default=False, description="Whether to use count fingerprints")
+    use_counts: bool = Field(default=True, description="Whether to use count fingerprints")
 
     @field_validator("fingerprints", mode="before")
     @classmethod
diff --git a/src/mother/feature_generation/core.py b/src/mother/feature_generation/core.py
index 8b3e5c7..6f7d219 100644
--- a/src/mother/feature_generation/core.py
+++ b/src/mother/feature_generation/core.py
@@ -64,7 +64,7 @@ class FingerprintsGeneric(BaseEstimator, TransformerMixin, _TransformOnlyValidMo
     the type and configuration of the fingerprint is determined by the parameters
     """
 
-    def __init__(self, fp_type: str, parameters: dict, use_counts: bool = False) -> None:
+    def __init__(self, fp_type: str, parameters: dict, use_counts: bool = True) -> None:
         self.fp_type: str = fp_type
         self.parameters: dict = parameters
         self.use_counts: bool = use_counts
@@ -119,7 +119,7 @@ def handle_bad_conformer(self, compound, error) -> Chem.rdchem.Mol:
 
 class MorganFingerprints(FingerprintsGeneric):
     def __init__(
-        self, radius: int = 2, fpSize=1024, include_chirality: bool = False, use_counts: bool = False, **kwargs
+        self, radius: int = 2, fpSize: int = 2048, include_chirality: bool = False, use_counts: bool = True, **kwargs
     ) -> None:
         super().__init__(
             "MorganFP",
diff --git a/test/unit/test_use_counts.py b/test/unit/test_use_counts.py
index fcbba02..663595a 100644
--- a/test/unit/test_use_counts.py
+++ b/test/unit/test_use_counts.py
@@ -87,13 +87,13 @@ def test_count_and_binary_differ(self, mols_with_repeated_substructures):
             "Count and binary fingerprints should produce different outputs"
         )
 
-    def test_use_counts_default_is_false(self):
-        """RED if default changed: use_counts should default to False."""
+    def test_use_counts_default_is_true(self):
+        """use_counts should default to True."""
         fg = FingerprintsGeneric(
             fp_type="MorganFP",
             parameters={"radius": 2, "fpSize": 2048},
         )
-        assert fg.use_counts is False
+        assert fg.use_counts is True
 
     def test_output_shape_consistent(self, mols_with_repeated_substructures):
         """Both modes should produce the same output shape."""
@@ -123,18 +123,17 @@ def test_output_shape_consistent(self, mols_with_repeated_substructures):
 class TestUseCountsMorganFingerprints:
     """Tests for use_counts on the MorganFingerprints convenience class."""
 
-    def test_morgan_binary_default(self, mols_with_repeated_substructures):
-        """MorganFingerprints with default use_counts=False should produce binary output."""
-        fg = MorganFingerprints(radius=2, fpSize=1024)
+    def test_morgan_count_default(self, mols_with_repeated_substructures):
+        """MorganFingerprints with default use_counts=True should produce count output."""
+        fg = MorganFingerprints(radius=2, fpSize=2048)
         fg.fit()
         result = fg.transform(mols_with_repeated_substructures)
 
-        unique_values = np.unique(result[~np.isnan(result)])
-        assert set(unique_values).issubset({0, 1})
+        assert np.nanmax(result) > 1
 
     def test_morgan_count_mode(self, mols_with_repeated_substructures):
         """RED if use_counts not wired through MorganFingerprints: should produce counts > 1."""
-        fg = MorganFingerprints(radius=2, fpSize=1024, use_counts=True)
+        fg = MorganFingerprints(radius=2, fpSize=2048, use_counts=True)
         fg.fit()
         result = fg.transform(mols_with_repeated_substructures)
 
@@ -143,7 +142,7 @@ def test_morgan_count_mode(self, mols_with_repeated_substructures):
 
     def test_morgan_use_counts_preserved_after_set_params(self, mols_with_repeated_substructures):
         """use_counts should persist after set_params on other parameters."""
-        fg = MorganFingerprints(radius=2, fpSize=1024, use_counts=True)
+        fg = MorganFingerprints(radius=2, fpSize=2048, use_counts=True)
         fg.set_params(radius=3)
         fg.fit()
         result = fg.transform(mols_with_repeated_substructures)
@@ -153,7 +152,7 @@ def test_morgan_use_counts_preserved_after_set_params(self, mols_with_repeated_s
 
     def test_morgan_use_counts_toggled_via_set_params(self, mols_with_repeated_substructures):
         """RED if use_counts isn't a proper sklearn param: set_params should toggle it."""
-        fg = MorganFingerprints(radius=2, fpSize=1024, use_counts=False)
+        fg = MorganFingerprints(radius=2, fpSize=2048, use_counts=False)
         fg.set_params(use_counts=True)
         fg.fit()
         result = fg.transform(mols_with_repeated_substructures)
@@ -165,7 +164,7 @@ def test_morgan_clone_preserves_use_counts(self):
         """sklearn.clone should preserve use_counts parameter."""
         from sklearn.base import clone
 
-        fg = MorganFingerprints(radius=2, fpSize=1024, use_counts=True)
+        fg = MorganFingerprints(radius=2, fpSize=2048, use_counts=True)
         fg_cloned = clone(fg)
 
         assert fg_cloned.use_counts is True
@@ -173,7 +172,7 @@ def test_morgan_clone_preserves_use_counts(self):
 
     def test_morgan_get_params_includes_use_counts(self):
         """use_counts should be visible in get_params()."""
-        fg = MorganFingerprints(radius=2, fpSize=1024, use_counts=True)
+        fg = MorganFingerprints(radius=2, fpSize=2048, use_counts=True)
         params = fg.get_params()
         assert "use_counts" in params
         assert params["use_counts"] is True
diff --git a/uv.lock b/uv.lock
index fb78256..88c2382 100644
--- a/uv.lock
+++ b/uv.lock
@@ -772,6 +772,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ec/a1/6b71004ab0fea510230be9ce05a4059029ac847c009fcc80b1b73d6fa5ab/colorlog-6.11.0-py3-none-any.whl", hash = "sha256:f1e27d75aa2cb138f3f640c0e305b65b680ccbef6ecc034eba7e03494ffcd2a1", size = 12016, upload-time = "2026-07-17T12:16:45.3Z" },
 ]
 
+[[package]]
+name = "colour"
+version = "0.1.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a0/d4/5911a7618acddc3f594ddf144ecd8a03c29074a540f4494670ad8f153efe/colour-0.1.5.tar.gz", hash = "sha256:af20120fefd2afede8b001fbef2ea9da70ad7d49fafdb6489025dae8745c3aee", size = 24776, upload-time = "2017-11-19T23:20:08.015Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/74/46/e81907704ab203206769dee1385dc77e1407576ff8f50a0681d0a6b541be/colour-0.1.5-py2.py3-none-any.whl", hash = "sha256:33f6db9d564fadc16e59921a56999b79571160ce09916303d35346dddc17978c", size = 23772, upload-time = "2017-11-19T23:20:04.56Z" },
+]
+
 [[package]]
 name = "comm"
 version = "0.2.3"
@@ -1485,6 +1494,20 @@ http = [
     { name = "aiohttp" },
 ]
 
+[[package]]
+name = "galois"
+version = "0.4.11"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numba" },
+    { name = "numpy" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/08/84/996f1c4a7e8fe973a7f8e4d7901e125a91d0abb62615e35076ff47e9383a/galois-0.4.11.tar.gz", hash = "sha256:f5055546c4b39d1e36decae9d4f21f3d66d9c6c7c9aec39189cb5d2284e9022f", size = 7402587, upload-time = "2026-05-02T18:21:28.615Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/05/89/dc69acbd34bde07b9dee9f9f88d7ce61ff97fd99d6d576d72d2db1461349/galois-0.4.11-py3-none-any.whl", hash = "sha256:d2e12a2b7fd44b108cc15d7de27dabcb8a98a556ea2c69c94fe695139629d9aa", size = 4197346, upload-time = "2026-05-02T18:21:26.868Z" },
+]
+
 [[package]]
 name = "ghp-import"
 version = "2.1.0"
@@ -2786,7 +2809,7 @@ wheels = [
 
 [[package]]
 name = "mother-ml"
-version = "1.0.4"
+version = "1.1.2"
 source = { editable = "." }
 dependencies = [
     { name = "boruta" },
@@ -2822,6 +2845,11 @@ rna = [
     { name = "scanpy", version = "1.11.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
     { name = "scanpy", version = "1.12.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
 ]
+tabicl = [
+    { name = "matplotlib" },
+    { name = "tabicl", extra = ["shap"] },
+    { name = "torch" },
+]
 tabpfn = [
     { name = "tabpfn" },
     { name = "torch" },
@@ -2876,6 +2904,7 @@ requires-dist = [
     { name = "kmedoids", marker = "extra == 'clustering'", specifier = ">=0.5.3.1,<0.6" },
     { name = "leidenalg", marker = "extra == 'report'", specifier = ">=0.10.2,<0.11" },
     { name = "matplotlib", marker = "extra == 'report'", specifier = ">3.7.0" },
+    { name = "matplotlib", marker = "extra == 'tabicl'", specifier = ">3.7.0" },
     { name = "numpy", specifier = ">=2.2,<2.3" },
     { name = "optuna", specifier = ">=4.2.0,<5" },
     { name = "pandas", specifier = ">=2.2,<3.0" },
@@ -2887,12 +2916,14 @@ requires-dist = [
     { name = "scikit-learn", specifier = ">=1.9.0,<1.10" },
     { name = "scipy", specifier = ">=1.11.1,<2" },
     { name = "seaborn", marker = "extra == 'report'", specifier = ">=0.13.2,<0.14" },
+    { name = "tabicl", extras = ["shap"], marker = "extra == 'tabicl'", specifier = ">=2.1.1" },
     { name = "tabpfn", marker = "extra == 'tabpfn'", specifier = "==8.2.0" },
+    { name = "torch", marker = "extra == 'tabicl'", specifier = ">=2.11.0,<3" },
     { name = "torch", marker = "extra == 'tabpfn'", specifier = ">=2.3.0,<3" },
     { name = "torch", marker = "extra == 'torch'", specifier = ">=2.3.0,<3" },
     { name = "umap-learn", marker = "extra == 'report'", specifier = ">=0.5.12,<0.6" },
 ]
-provides-extras = ["torch", "report", "rna", "clustering", "tabpfn"]
+provides-extras = ["torch", "report", "rna", "clustering", "tabpfn", "tabicl"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -5237,6 +5268,69 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/43/dd076925ec7544f14cd264cdc2019267bb26029d07e483027d6bddf19630/shap-0.47.2-cp312-cp312-win_amd64.whl", hash = "sha256:93be4e3fe4cc59582d6096c626b1443e30bcee2c7adda40af9d09ee0721647bc", size = 545222, upload-time = "2025-04-17T18:14:46.349Z" },
 ]
 
+[[package]]
+name = "shapiq"
+version = "1.4.1"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.12'",
+]
+dependencies = [
+    { name = "colour" },
+    { name = "galois" },
+    { name = "joblib" },
+    { name = "matplotlib" },
+    { name = "networkx" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "requests" },
+    { name = "scikit-learn" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" } },
+    { name = "sparse-transform" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a8/20/8e7bf8e1d2ad5e50c5ddb884760fe67657eee38b0ebe9d9996a0ab227221/shapiq-1.4.1.tar.gz", hash = "sha256:7f612c84fcfe4746ff826db72efa2944f9aa693c5c00c6e17a882613471534f8", size = 5781786, upload-time = "2025-11-10T19:43:31.882Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/cc/13fefd7203ffe71d5107221be939dd64945378b93f7c0a7cb82d7f281071/shapiq-1.4.1-py3-none-any.whl", hash = "sha256:c86667eb28ea0de56005fbe9fd072783242b74aa87fede11e07b6b915b93e7b3", size = 5871550, upload-time = "2025-11-10T19:43:29.858Z" },
+]
+
+[[package]]
+name = "shapiq"
+version = "1.7.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+]
+dependencies = [
+    { name = "colour" },
+    { name = "joblib" },
+    { name = "matplotlib" },
+    { name = "networkx" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "requests" },
+    { name = "scikit-learn" },
+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" } },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/45/55/6b12e99772dd45a90d6473afd16a1c6438da1ab4d91c6b9b81ac4e958088/shapiq-1.7.0.tar.gz", hash = "sha256:17cad036c50ec5782a4e72550ba5b1e73955b8f06387acb6e20d436eb33eb44d", size = 15318066, upload-time = "2026-08-27T13:10:59.869Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/4b/d2866469cd6fb1beed9cc2345173128fdd4a21670f73cf0e04197f504039/shapiq-1.7.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0a29d2e83decaf1fc6b7a3344c774b9466fed486684b01d4777ae4c96404190b", size = 16164581, upload-time = "2026-08-27T13:10:31.132Z" },
+    { url = "https://files.pythonhosted.org/packages/80/98/f16b1a36869135d2512bf050cec1f1e0070ed12e849ddcca8802fb30ac5c/shapiq-1.7.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:2a97f078678672655391a62638060fbdc1129a6095e81fbbe9e5ba088b05a2a6", size = 16147370, upload-time = "2026-08-27T13:10:33.595Z" },
+    { url = "https://files.pythonhosted.org/packages/83/fd/c5e31e17e6fb09c376e997c996c60b6008bd64d0fbb348b597af98ab48a7/shapiq-1.7.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fc2e556f5b3a9c5e3f55f402f1e43276917e4228ba21a97f104dea0d8cc8c9e5", size = 17928337, upload-time = "2026-08-27T13:10:35.945Z" },
+    { url = "https://files.pythonhosted.org/packages/41/42/560dc3a2a3627200ddfe8ded67ca79e7adf7426f5e1b21b627ce2b68055e/shapiq-1.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:474ead88733724319fe016ba5d670200e23c3796b813feacfde8be553900d956", size = 16043784, upload-time = "2026-08-27T13:10:38.129Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/36/c5e3f6f52012367c80f5d36d4a2b2611986af08b5dd50c87be24306fc483/shapiq-1.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a3e00c0b0a3d3ee01ff8334926329bbbb1b8f8d9cabe81f1a5f16e622efe9a91", size = 16164596, upload-time = "2026-08-27T13:10:40.407Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/17/4cde51ee3c819f456a63824c56effa73cc419441b5711d0444b599e3c34e/shapiq-1.7.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:870f622f3c4962b56046babcbda9b8f36b32ae59b9e8413993fc9b646128a3dd", size = 16147373, upload-time = "2026-08-27T13:10:43.429Z" },
+    { url = "https://files.pythonhosted.org/packages/18/17/3b890f3adf731725467c93f1becfef19bdcaaa5194a2bb651f0e3061991a/shapiq-1.7.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9ba078726fa6401278d83c8fd4ad8fa28cc851374c5cc56aa2890984c6022441", size = 17940714, upload-time = "2026-08-27T13:10:45.883Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/e6/f609f790273ae06fb97d19d3b1e834baff7569f878ca6ab8bfcc7e61ba7a/shapiq-1.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:adb39eaa225d8cd50aee7cee5153e0c3baa4fdf7b43f1c396540a17381de7409", size = 16043797, upload-time = "2026-08-27T13:10:48.212Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/96/7182581836dc98d18a8813e5e0565887c05ba7e92b7ce4301677336c6cdb/shapiq-1.7.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:0a5db3f3587ba581e521c5e6e8a4c46634da194885c4dc93fcafab7dcd3345fc", size = 16164721, upload-time = "2026-08-27T13:10:50.552Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/3d/c1978dac495effbfec4fff4490ade240f12506ad2ac5871a432173be2083/shapiq-1.7.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:d50e2c21cf7b15a9040d0e7da7505e4754ca32ea9b0564dbbeab49f6add2a223", size = 16147558, upload-time = "2026-08-27T13:10:53.048Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/87/e35d493374eb2d9a54b49fdd120f651f9e29bcc3102a82c30cebb0da9e05/shapiq-1.7.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:116ac5797692d5b687fa4bafed32a0149612aefdb3b1e0c5f3057f953ce916db", size = 17941056, upload-time = "2026-08-27T13:10:55.329Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/a2/5d004323f1027f0ca176e2340492aa3d0d3eced8730af8667ff057de559e/shapiq-1.7.0-cp314-cp314-win_amd64.whl", hash = "sha256:7fce0912d865eb3d793b1f6f1171b96c9e4df3b83476dcf8ee7bca1709bc6881", size = 15928709, upload-time = "2026-08-27T13:10:57.563Z" },
+]
+
 [[package]]
 name = "shellingham"
 version = "1.5.4"
@@ -5282,6 +5376,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7b/d6/3185ab5ad1280319b31986898f3206dd7227cd75e293d4dba2a5e6bf27a0/soupsieve-2.9-py3-none-any.whl", hash = "sha256:a2b2c76d67df2382d245409fd71e321a571717e58463efa32ace87dcadac2c12", size = 37387, upload-time = "2026-07-19T01:35:17.106Z" },
 ]
 
+[[package]]
+name = "sparse-transform"
+version = "0.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "galois" },
+    { name = "numpy" },
+    { name = "scikit-learn" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" } },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d5/da/82132137fdc207c4a14abb52fedcd8cc3dd697175f548cfb8689bd2afab8/sparse_transform-0.2.1.tar.gz", hash = "sha256:470d54c884600d97254469a3d85fac775480d18a8aa2c43574350695e1015c66", size = 24803, upload-time = "2025-04-03T08:11:39.47Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/64/ec/c85f95289e4638faf889ca02b588ad3af0f3ef8f92b99bee3ce936ef3860/sparse_transform-0.2.1-py3-none-any.whl", hash = "sha256:d01b19f8add68e2b4c98a17272830af0e03d163667f03b1dc2b0c9eb8471727b", size = 27034, upload-time = "2025-04-03T08:11:38.551Z" },
+]
+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.51"
@@ -5396,6 +5506,35 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "tabicl"
+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "einops" },
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "psutil" },
+    { name = "scikit-learn" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "torch" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e0/c3/ef8bcc7645bc1eedae4563df6429f6f5438aaea5e293682a9367bf2c2ddb/tabicl-2.1.1.tar.gz", hash = "sha256:7abdb1fa878e7a1edfa1c6606bf4189e9cccc20935e8011d7e690f4693c9c5c5", size = 224680, upload-time = "2026-04-29T15:57:46.302Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/60/d3/f6eadcef58322b1b0253df1c17d9230db9934274f52e0d952458f6c6ab41/tabicl-2.1.1-py3-none-any.whl", hash = "sha256:cb4405cc93335c688bc9bcb703c7944032fcf542b43ebb66820f1a5acb5651b1", size = 252909, upload-time = "2026-04-29T15:57:47.775Z" },
+]
+
+[package.optional-dependencies]
+shap = [
+    { name = "matplotlib" },
+    { name = "numba" },
+    { name = "shap" },
+    { name = "shapiq", version = "1.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "shapiq", version = "1.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+]
+
 [[package]]
 name = "tabpfn"
 version = "8.2.0"