diff --git a/optimization/optimizer.py b/optimization/optimizer.py
index edf9ba0..0052cc2 100644
--- a/optimization/optimizer.py
+++ b/optimization/optimizer.py
@@ -6,6 +6,7 @@
 
 plt.style.use('dark_background')
 
+
 class Optimizer:
     def __init__(self, model, featureset, target, validator, goal='maximize', search_spaces=None):
         if isinstance(search_spaces, type(None)):
@@ -45,8 +46,7 @@ def plot_objective(self):
                             models=self.opt.models)
         plot_objective(res, dimensions=self.parameter_names);
         plt.show()
-        
-        
+
     def plot_evaluations(self):
         res = create_result(Xi=self.opt.Xi, 
                             yi=self.opt.yi, 
diff --git a/zoo/binary_classification.py b/zoo/binary_classification.py
index 3a34088..0e9110f 100644
--- a/zoo/binary_classification.py
+++ b/zoo/binary_classification.py
@@ -1,7 +1,12 @@
 from ..model import *
 from .. import config
-
 from xgboost import XGBClassifier as _XGBC
+from lightgbm import LGBMClassifier as _LGBMC
+from catboost import CatBoostClassifier as _CBC
+from sklearn.linear_model import LogisticRegression as _LOGR
+from sklearn.neighbors import KNeighborsClassifier as _KNC
+
+
 class XGBClassifier(Model):
     Estimator = _XGBC
     search_spaces = {
@@ -38,8 +43,8 @@ def _fit(self, X, y, **kwargs):
 
     def predict(self, X):
         return self.estimator.predict_proba(X)[:, 1]
-    
-from lightgbm import LGBMClassifier as _LGBMC
+
+
 class LGBMClassifier(Model):
     Estimator = _LGBMC
     search_spaces = {
@@ -58,8 +63,8 @@ class LGBMClassifier(Model):
     
     def predict(self, X):
         return self.estimator.predict_proba(X)[:, 1]
-    
-from catboost import CatBoostClassifier as _CBC
+
+
 class CBClassifier(Model):
     Estimator = _CBC
     short_name = 'cb'
@@ -75,7 +80,7 @@ def _fit(self, X, y, **kwargs):
     def predict(self, X):
         return self.estimator.predict_proba(X)[:, 1]
 
-from sklearn.linear_model import LogisticRegression as _LOGR
+
 class LogisticRegression(Model):
     Estimator = _LOGR
     short_name = 'logr'
@@ -97,3 +102,14 @@ class LogisticRegression(Model):
 
     def predict(self, X):
         return self.estimator.predict_proba(X)[:, 1]
+
+
+class KNeighborsClassifier(Model):
+    Estimator = _KNC
+    system_params = {
+        "n_jobs": -1,
+    }
+    short_name = 'knc'
+
+    def predict(self, X):
+        return self.estimator.predict_proba(X)[:, 1]
diff --git a/zoo/classification.py b/zoo/classification.py
index dfdbecc..8294425 100644
--- a/zoo/classification.py
+++ b/zoo/classification.py
@@ -1,25 +1,11 @@
 from ..model import *
 from .. import config
-
 from xgboost import XGBClassifier as _XGBC
+from sklearn.neighbors import KNeighborsClassifier as _KNC
+
+
 class XGBClassifier(Model):
     Estimator = _XGBC
-    default_params = {
-        "max_depth": 3, 
-        "learning_rate": 0.1, 
-        "n_estimators": 100, 
-        "booster": 'gbtree', 
-        "gamma": 0, 
-        "min_child_weight": 1, 
-        "max_delta_step": 0, 
-        "subsample": 1, 
-        "colsample_bytree": 1, 
-        "colsample_bylevel": 1, 
-        "reg_alpha": 0, 
-        "reg_lambda": 1, 
-        "scale_pos_weight": 1, 
-        "base_score": 0.5,
-    }
     search_spaces = {
         'max_depth': (0, 50),
         'learning_rate': (0.01, 1.0, 'log-uniform'),
@@ -39,24 +25,20 @@ class XGBClassifier(Model):
     system_params = {
         "objective": 'binary:logistic', 
         "silent": True,
-        "n_jobs": 1, 
+        "n_jobs": -1,
         "random_state": 0, 
         "seed": config.seed
     }
     short_name = 'xgb'
-    
-    
-from lightgbm import LGBMClassifier as _LGBMC
-class LGBMClassifier(Model):
-    Estimator = _LGBMC
-    short_name = 'lgb'
-    
-    
-from catboost import CatBoostClassifier as _CBC
-class CBClassifier(Model):
-    Estimator = _CBC
-    short_name = 'cb'
-    
+
+
+class KNeighborsClassifier(Model):
+    Estimator = _KNC
+    system_params = {
+        "n_jobs": -1,
+    }
+    short_name = 'knc'
+
     def predict(self, X):
         return self.estimator.predict_proba(X)
-    
\ No newline at end of file
+