diff --git a/docs/freqai.md b/docs/freqai.md
index 8537beac2..e38132cb1 100644
--- a/docs/freqai.md
+++ b/docs/freqai.md
@@ -539,11 +539,19 @@ The user can tell FreqAI to use DBSCAN to cluster training data and remove outli
 parameter `DBSCAN_outlier_pct` allows the user to indicate the percent of expected outliers to be removed during each training
 (typically set below 0.05). Higher value increases confidence in the model predictions but reduces the entry frequency.
 
-The FreqAI DBSCAN wrapper performs an interative solution to solving the `eps` hyper parameter. `eps` controls the fraction of 
+The FreqAI DBSCAN wrapper performs an iterative solution to solving the `eps` hyper parameter. `eps` controls the fraction of 
 training data considered to be an outlier - thus the iterative solution finds the exact value associated with the user designated
 `DBSCAN_outlier_pct`. This iterative solution is performed once per training. FreqAI stores the `eps` to be used when DBSCAN
 is again called to determine if incoming prediction candles are outliers. 
 
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "DBSCAN_outlier_pct": 0.05
+        }
+    }
+```
+
 ### Stratifying the data
 
 The user can stratify the training/testing data using:
diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 0cec0b9e1..823cf2a55 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -11,6 +11,7 @@ import numpy.typing as npt
 import pandas as pd
 from pandas import DataFrame
 from sklearn import linear_model
+from sklearn.cluster import DBSCAN
 from sklearn.metrics.pairwise import pairwise_distances
 from sklearn.model_selection import train_test_split
 
@@ -19,7 +20,7 @@ from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
 from freqtrade.exceptions import OperationalException
 from freqtrade.resolvers import ExchangeResolver
 from freqtrade.strategy.interface import IStrategy
-from sklearn.cluster import DBSCAN
+
 
 SECONDS_IN_DAY = 86400
 SECONDS_IN_HOUR = 3600
@@ -499,7 +500,8 @@ class FreqaiDataKitchen:
         for prediction confidence in the Dissimilarity Index
         """
         logger.info("computing average mean distance for all training points")
-        pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=self.thread_count)
+        pairwise = pairwise_distances(
+            self.data_dictionary["train_features"], n_jobs=self.thread_count)
         avg_mean_dist = pairwise.mean(axis=1).mean()
 
         return avg_mean_dist
@@ -613,21 +615,33 @@ class FreqaiDataKitchen:
 
         else:
             outlier_target = self.freqai_config['feature_parameters'].get('DBSCAN_outlier_pct')
-            eps = 1.8
+            if 'DBSCAN_eps' in self.data:
+                eps = self.data['DBSCAN_eps']
+            else:
+                eps = 10
+                logger.info('DBSCAN starting from high value. This should be faster next train.')
+
             error = 1.
-            MinPts = len(train_ft_df.columns) * 2
+            MinPts = len(self.data_dictionary['train_features'].columns)
             logger.info(
                     f'DBSCAN finding best clustering for {outlier_target}% outliers.')
 
             # find optimal value for epsilon using an iterative approach:
-            while abs(error) > 0.01:
-                clustering = DBSCAN(eps=eps, min_samples=MinPts, n_jobs=-1).fit(
-                    train_ft_df
-                )
+            while abs(np.sqrt(error)) > 0.1:
+                clustering = DBSCAN(eps=eps, min_samples=MinPts,
+                                    n_jobs=int(self.thread_count / 2)).fit(
+                                        self.data_dictionary['train_features']
+                                    )
                 outlier_pct = np.count_nonzero(clustering.labels_ == -1) / len(clustering.labels_)
-                error = (outlier_pct - outlier_target) / outlier_target
-                multiplier = 1 + error * (1.01 - 1.)
+                error = (outlier_pct - outlier_target) ** 2 / outlier_target
+                multiplier = (outlier_pct - outlier_target) if outlier_pct > 0 else 1 * \
+                    np.sign(outlier_pct - outlier_target)
+                multiplier = 1 + error * multiplier
                 eps = multiplier * eps
+                logger.info(
+                    f'DBSCAN error {error:.2f} for eps {eps:.2f} and outliet pct {outlier_pct:.2f}')
+
+            logger.info(f'DBSCAN found eps of {eps}.')
 
             self.data['DBSCAN_eps'] = eps
             self.data['DBSCAN_min_samples'] = MinPts