title: Different anomalies found in each run - Isolation forest
body: <p>I have built an Isolation forest using sklearn and Python, and every time I run the model to find anomalies, I get slightly different results. </p>
  
  <p>My data has 100 rows, and each time I get a different combination of 4-8 anomalies within the 90th-100th row, does anyone know why this could be? </p>
  
  <p>Thank you for your help. 
  I have attached some of the code I have used. </p>
  
  <pre><code>from sklearn.model_selection import train_test_split
  X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  X_test = sc.transform(X_test)
  model = IsolationForest(n_estimators = 100, max_samples = 'auto', 
  contamination = 'auto')
  model.fit(df[['label']])
  anomaly = df.loc[df['anomaly']==-1]
  </code></pre>