title: DBSCAN eps and min_samples
body: <p>I have been trying to use DBSCAN in order to detect outliers, from my understanding DBSCAN outputs -1 as outlier and 1 as inliner, but after I ran the code, I'm getting numbers that are not -1 or 1, can someone please explain why? Also is it normal to find the best value of eps using trial and error, because I couldn't figure out a way to find the best possible eps value.</p>
  
  <pre><code>import numpy as np
  
  import pandas as pd
  
  import matplotlib.pyplot as plt
  
  %matplotlib inline
  
  from sklearn.cluster import DBSCAN
  
  
  
  df = pd.read_csv('Final After Simple Filtering.csv',index_col=None,low_memory=True)
  
  
  # Dropping columns with low feature importance
  del df['AmbTemp_DegC']
  del df['NacelleOrientation_Deg']
  del df['MeasuredYawError']
  
  
  
  #applying DBSCAN
  
  
  DBSCAN = DBSCAN(eps = 1.8, min_samples =10,n_jobs=-1)
  
  df['anomaly'] = DBSCAN.fit_predict(df)
  
  
  np.unique(df['anomaly'],return_counts=True)
  
  </code></pre>
  
  <pre><code>(array([  -1,    0,    1, ..., 8462, 8463, 8464]),
  array([1737565, 3539278, 4455734, ...,      13,       8,       8]))
  </code></pre>
  
  <p>Thank you.</p>