title: Outlier-Detection in Scikit-learn( Isolation Forest) in a pipeline
body: <p>I have encountered the problem, as I can't use the Isolation Forest algorithm in the Sklearn pipeline. I am trying to predict the credit card default using the Kaggle Credit Card Fraud Detection dataset. I am trying to fix everything after data partitioning in order to avoid data leakage. (By using pipelines for every cross-validation as I get an almost 100% F1-score using Logistic Regression in K-fold cross-validation without using pipelines) Most of the machine learning algorithms can be used (Logistic Regression, Random Forest Classifier, etc) but not for some anomaly detection algorithms such as IsolationForest. I wondered how can I fit these anomaly detection algorithms inside the Pipelines. Thanks.</p>
  <p><a href="https://i.stack.imgur.com/kozpu.png" rel="nofollow noreferrer">Some details for X and Y (Y- 0 as a normal transaction, 1 as fraudulent transaction)</a></p>
  <pre><code>pipe =Pipeline([
      ('sc', StandardScaler()),
      ('smote', SMOTE()),
      ('IF', IsolationForest())
  ])
  
  print(cross_val_score(pipe, X,Y, scoring='f1_weighted' ,cv=5))
  
  # Result: [3.01179163e-06 3.53204982e-06 6.55363495e-06 3.51940600e-06 4.52981524e-06]
  </code></pre>