diff --git a/boruta/boruta_py.py b/boruta/boruta_py.py
index 525b525..4eff5ac 100644
--- a/boruta/boruta_py.py
+++ b/boruta/boruta_py.py
@@ -114,6 +114,17 @@ class BorutaPy(BaseEstimator, TransformerMixin):
         - 1: displays iteration number
         - 2: which features have been selected already
 
+    early_stopping : bool, default = False
+        Whether to use early stopping to terminate the selection process
+        before reaching `max_iter` iterations if the algorithm cannot
+        confirm a tentative feature for `n_iter_no_change` iterations.
+        Will speed up the process at a cost of a possibility of a
+        worse result.
+        
+    n_iter_no_change : int, default = 20
+        Ignored if `early_stopping` is False. The maximum amount of
+        iterations without confirming a tentative feature. 
+
     Attributes
     ----------
 
@@ -180,7 +191,8 @@ class BorutaPy(BaseEstimator, TransformerMixin):
     """
 
     def __init__(self, estimator, n_estimators=1000, perc=100, alpha=0.05,
-                 two_step=True, max_iter=100, random_state=None, verbose=0):
+                 two_step=True, max_iter=100, random_state=None, verbose=0,
+                 early_stopping=False, n_iter_no_change=20):
         self.estimator = estimator
         self.n_estimators = n_estimators
         self.perc = perc
@@ -189,6 +201,8 @@ def __init__(self, estimator, n_estimators=1000, perc=100, alpha=0.05,
         self.max_iter = max_iter
         self.random_state = random_state
         self.verbose = verbose
+        self.early_stopping = early_stopping
+        self.n_iter_no_change = n_iter_no_change
         self.__version__ = '0.3'
         self._is_lightgbm = 'lightgbm' in str(type(self.estimator))
 
@@ -279,9 +293,25 @@ def _fit(self, X, y):
             y = self._validate_pandas_input(y)
 
         self.random_state = check_random_state(self.random_state)
+        
+        early_stopping = False
+        if self.early_stopping:
+            if self.n_iter_no_change >= self.max_iter:
+                if self.verbose > 0:
+                    print(
+                        f"n_iter_no_change is bigger or equal to max_iter"
+                        f"({self.n_iter_no_change} >= {self.max_iter}), "
+                        f"early stopping will not be used."
+                    )
+            else:
+                early_stopping = True
+        
         # setup variables for Boruta
         n_sample, n_feat = X.shape
         _iter = 1
+        # early stopping vars
+        _same_iters = 1
+        _last_dec_reg = None
         # holds the decision about each feature:
         # 0  - default state = tentative in original code
         # 1  - accepted in original code
@@ -335,6 +365,21 @@ def _fit(self, X, y):
                 self._print_results(dec_reg, _iter, 0)
             if _iter < self.max_iter:
                 _iter += 1
+                
+            # early stopping
+            if early_stopping:
+                if _last_dec_reg is not None and (_last_dec_reg == dec_reg).all():
+                    _same_iters += 1
+                    if self.verbose > 0:
+                        print(
+                            f"Early stopping: {_same_iters} out "
+                            f"of {self.n_iter_no_change}"
+                        )
+                else:
+                    _same_iters = 1
+                    _last_dec_reg = dec_reg.copy()
+                if _same_iters > self.n_iter_no_change:
+                    break
 
         # we automatically apply R package's rough fix for tentative ones
         confirmed = np.where(dec_reg == 1)[0]