Why SKlearn outperform WEKA in predicting using a RandomForest model? : I have this [dataset][1], and I'm using SKlearn to generate a random forest model as follows: 

   

     from sklearn.ensemble import RandomForestClassifier as RandomForest
        from sklearn.cross_validation import cross_val_score, cross_val_predict
        import pandas as pd
        import numpy as np
        df = pd.read_csv('trainingSetExample.csv')
        X_train = df.iloc[:, df.columns != 'label']
        y_train = df.ix[:]['label']
        clf = RandomForest() 
        print np.mean(cross_val_score(clf, X_train, y_train, cv=10))
        print 'precision', np.mean(cross_val_score(clf, X_train, y_train, cv=10, scoring='precision_macro'))
  
Accuracy and precision are both 0.99, but when I use WEKA randomforest, accuracy and precision are both 0.95. It looks like the default values of the parameters for both is the same, in addition, I tried WEKA with 10000 iterations instead of 100 and it didn't improve. Why are the results that much different?            

          

  [1]: https://drive.google.com/file/d/1tS7BGQmRkbw3XREZGWET9XMJPfpxLIXW/view?usp=sharing