add base for sklearn frontend with methods to override

ivy/functional/frontends/sklearn/__init__.py

@@ -0,0 +1,20 @@
+import ivy
+from ivy.functional.frontends.numpy import array
+
+_int8 = ivy.IntDtype("int8")
+_int16 = ivy.IntDtype("int16")
+_int32 = ivy.IntDtype("int32")
+_int64 = ivy.IntDtype("int64")
+_uint8 = ivy.UintDtype("uint8")
+_uint16 = ivy.UintDtype("uint16")
+_uint32 = ivy.UintDtype("uint32")
+_uint64 = ivy.UintDtype("uint64")
+_bfloat16 = ivy.FloatDtype("bfloat16")
+_float16 = ivy.FloatDtype("float16")
+_float32 = ivy.FloatDtype("float32")
+_float64 = ivy.FloatDtype("float64")
+_complex64 = ivy.ComplexDtype("complex64")
+_complex128 = ivy.ComplexDtype("complex128")
+_bool = ivy.Dtype("bool")
+
+_frontend_array = array

ivy/functional/frontends/sklearn/base.py

@@ -0,0 +1,30 @@
+class BaseEstimator:
+    def get_params(self, deep=True):
+        return {}
+
+    def set_params(self, **params):
+        return self
+
+
+class ClassifierMixin:
+
+    def score(self, X, y, sample_weight=None):
+        raise NotImplementedError
+
+    def fit(self, X, y, **kwargs):
+        raise NotImplementedError
+
+    def predict(self, X):
+        raise NotImplementedError
+
+
+class RegressorMixin:
+
+    def score(self, X, y, sample_weight=None):
+        raise NotImplementedError
+
+    def fit(self, X, y, **kwargs):
+        raise NotImplementedError
+
+    def predict(self, X):
+        raise NotImplementedError