diff --git a/pyod/models/kpca.py b/pyod/models/kpca.py
index 02d081ff7439afd6eace950b677b995e92375cc9..66be544868ca3e56c0474ae21f7ea1655153877c 100644
--- a/pyod/models/kpca.py
+++ b/pyod/models/kpca.py
@@ -10,30 +10,30 @@ from sklearn.decomposition import KernelPCA
 from sklearn.utils import check_array, check_random_state
 from sklearn.utils.validation import check_is_fitted
 
-from .base import BaseDetector
 from ..utils.utility import check_parameter
+from .base import BaseDetector
 
 
 class PyODKernelPCA(KernelPCA):
     """A wrapper class for KernelPCA class of scikit-learn."""
 
     def __init__(
-            self,
-            n_components=None,
-            kernel="rbf",
-            gamma=None,
-            degree=3,
-            coef0=1,
-            kernel_params=None,
-            alpha=1.0,
-            fit_inverse_transform=False,
-            eigen_solver="auto",
-            tol=0,
-            max_iter=None,
-            remove_zero_eig=False,
-            copy_X=True,
-            n_jobs=None,
-            random_state=None,
+        self,
+        n_components=None,
+        kernel="rbf",
+        gamma=None,
+        degree=3,
+        coef0=1,
+        kernel_params=None,
+        alpha=1.0,
+        fit_inverse_transform=False,
+        eigen_solver="auto",
+        tol=0,
+        max_iter=None,
+        remove_zero_eig=False,
+        copy_X=True,
+        n_jobs=None,
+        random_state=None,
     ):
         super().__init__(
             kernel=kernel,
@@ -198,30 +198,41 @@ class KPCA(BaseDetector):
     """
 
     def __init__(
-            self,
-            contamination=0.1,
-            n_components=None,
-            n_selected_components=None,
-            kernel="rbf",
-            gamma=None,
-            degree=3,
-            coef0=1,
-            kernel_params=None,
-            alpha=1.0,
-            eigen_solver="auto",
-            tol=0,
-            max_iter=None,
-            remove_zero_eig=False,
-            copy_X=True,
-            n_jobs=None,
-            sampling=False,
-            subset_size=20,
-            random_state=None,
+        self,
+        contamination=0.1,
+        n_components=None,
+        n_selected_components=None,
+        kernel="rbf",
+        gamma=None,
+        degree=3,
+        coef0=1,
+        kernel_params=None,
+        alpha=1.0,
+        eigen_solver="auto",
+        tol=0,
+        max_iter=None,
+        remove_zero_eig=False,
+        copy_X=True,
+        n_jobs=None,
+        sampling=False,
+        subset_size=20,
+        random_state=None,
     ):
         super().__init__(contamination=contamination)
         self.n_components = n_components
         self.n_selected_components = n_selected_components
-        self.copy_x = copy_X
+        self.kernel = kernel
+        self.gamma = gamma
+        self.degree = degree
+        self.coef0 = coef0
+        self.kernel_params = kernel_params
+        self.alpha = alpha
+        self.eigen_solver = eigen_solver
+        self.tol = tol
+        self.max_iter = max_iter
+        self.remove_zero_eig = remove_zero_eig
+        self.copy_X = copy_X
+        self.n_jobs = n_jobs
         self.sampling = sampling
         self.subset_size = subset_size
         self.random_state = check_random_state(random_state)
@@ -229,20 +240,21 @@ class KPCA(BaseDetector):
         self.n_selected_components_ = None
 
         self.kpca = PyODKernelPCA(
-            n_components=n_components,
-            kernel=kernel,
-            gamma=gamma,
-            degree=degree,
-            coef0=coef0,
-            kernel_params=kernel_params,
-            alpha=alpha,
+            n_components=self.n_components,
+            kernel=self.kernel,
+            gamma=self.gamma,
+            degree=self.degree,
+            coef0=self.coef0,
+            kernel_params=self.kernel_params,
+            alpha=self.alpha,
             fit_inverse_transform=False,
-            eigen_solver=eigen_solver,
-            tol=tol,
-            max_iter=max_iter,
-            remove_zero_eig=remove_zero_eig,
-            copy_X=copy_X,
-            n_jobs=n_jobs,
+            eigen_solver=self.eigen_solver,
+            tol=self.tol,
+            max_iter=self.max_iter,
+            remove_zero_eig=self.remove_zero_eig,
+            copy_X=self.copy_X,
+            n_jobs=self.n_jobs,
+            random_state=self.random_state,
         )
 
     def _check_subset_size(self, array):
@@ -283,7 +295,7 @@ class KPCA(BaseDetector):
         """
 
         # validate inputs X and y (optional)
-        X = check_array(X, copy=self.copy_x)
+        X = check_array(X, copy=self.copy_X)
         self._set_n_classes(y)
 
         # perform subsampling to reduce time complexity