diff --git a/.circleci/config.yml b/.circleci/config.yml
deleted file mode 100644
index e851faf7..00000000
--- a/.circleci/config.yml
+++ /dev/null
@@ -1,22 +0,0 @@
-version: 2.1
-
-jobs:
-  lint:
-    machine:
-      image: ubuntu-2204:2023.02.1
-    steps:
-      - checkout
-      - run: sudo apt-get install pip
-      - run:
-          command: |
-              sudo python3 -m pip install black flake8
-      - run:
-          command: |
-              black --check examples sklearn_extra *py
-              # ensure there is no unused imports with flake8
-              flake8
-
-workflows:
-  build-doc-and-deploy:
-    jobs:
-      - lint
diff --git a/.coveragerc b/.coveragerc
index 2de8587e..ad2f95fe 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -18,4 +18,4 @@ exclude_lines =
     if 0:
     if __name__ == .__main__.:
     if self.verbose:
-show_missing = True
\ No newline at end of file
+show_missing = True
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 87caceca..d29162b8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,16 +1,16 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.4.0
+    rev: v4.4.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 20.8b1
+    rev: 23.1.0
     hooks:
     -   id: black
--   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.9.0
+-   repo: https://github.com/pycqa/flake8
+    rev: 6.0.0
     hooks:
     -   id: flake8
         types: [file, python]
diff --git a/doc/docs.md b/doc/docs.md
index a0047413..2aa121ca 100644
--- a/doc/docs.md
+++ b/doc/docs.md
@@ -5,6 +5,5 @@
 - scikit-learn(>=0.21)
 - Cython (>0.28)
 ### User Installation:
-You can install scikit-learn-extra using this command: 
+You can install scikit-learn-extra using this command:
 `pip install https://github.com/scikit-learn-contrib/scikit-learn-extra/archive/master.zip`
-
diff --git a/doc/index.rst b/doc/index.rst
index 3c9f84fa..db4e6cc1 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -28,4 +28,3 @@ scikit-learn-extra is a Python module for machine learning that extends scikit-l
 
    contributing
    changelog
-
diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst
index b8ea39e8..b234d691 100644
--- a/doc/modules/kernel_approximation.rst
+++ b/doc/modules/kernel_approximation.rst
@@ -6,8 +6,8 @@ Kernel map approximation for faster kernel methods
 
 .. currentmodule:: sklearn_extra.kernel_approximation
 
-Kernel methods, which are among the most flexible and influential tools in 
-machine learning with applications in virtually all areas of the field, rely 
+Kernel methods, which are among the most flexible and influential tools in
+machine learning with applications in virtually all areas of the field, rely
 on high-dimensional feature spaces in order to construct powerfull classifiers or
 regressors or clustering algorithms. The main drawback of kernel methods
 is their prohibitive computational complexity. Both spatial and temporal complexity
@@ -15,20 +15,20 @@ is their prohibitive computational complexity. Both spatial and temporal complex
 
 One of the popular way to improve the computational scalability of kernel methods is
 to approximate the feature map impicit behind the kernel method. In practice,
-this means that we will compute a low dimensional approximation of the 
+this means that we will compute a low dimensional approximation of the
 the otherwise high-dimensional embedding used to define the kernel method.
 
 :class:`Fastfood` approximates feature map of an RBF kernel by Monte Carlo approximation
 of its Fourier transform.
 
-Fastfood replaces the random matrix of Random Kitchen Sinks 
+Fastfood replaces the random matrix of Random Kitchen Sinks
 (`RBFSampler <https://scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html#sklearn.kernel_approximation.RBFSampler>`_)
 with an approximation that uses the Walsh-Hadamard transformation to gain
 significant speed and storage advantages.  The computational complexity for
 mapping a single example is O(n_components log d).  The space complexity is
-O(n_components).  
+O(n_components).
 
 See `scikit-learn User-guide <https://scikit-learn.org/stable/modules/kernel_approximation.html#kernel-approximation>`_ for more general informations on kernel approximations.
 
-See also :class:`EigenProRegressor <sklearn_extra.kernel_methods.EigenProRegressor>` and :class:`EigenProClassifier <sklearn_extra.kernel_methods.EigenProClassifier>` for another 
+See also :class:`EigenProRegressor <sklearn_extra.kernel_methods.EigenProRegressor>` and :class:`EigenProClassifier <sklearn_extra.kernel_methods.EigenProClassifier>` for another
 way to compute fast kernel methods algorithms.