In Python 1 Set predictors and outcome variables, and partition the data into 6 0 training, 4 0 validation 1 Set predictors and outcome predictors ' Cleanliness ' , 'Nurse communication, 'Doctor Communication', 'Discharge information', 'Met Color', 'Automatic', ' CC ' , 'Doors', 'Tax', 'Weight' outcome 'Rating' 2 Train a tree for classification 3 Run random forest and boosted tree models for classification 4 Which rf variable is the most important ( e g , importances rf model named steps ' classifier ' feature importances ) 5 Assess the performance of all models using Sklearn classification report Which model is better See an example below of how to set up one of the models classTree DecisionTreeClassifier ( random state 0 , max depth 5 , min samples split 5 0 ) tree model Pipeline ( steps ( ' preprocessor ' , preprocessor ) , ( ' classifier ' , classTree ) ) tree model fit ( train X , train y ) pred y tree model predict ( valid X ) print ( ' DecisionTreeClassifier Classification Report ' ) print ( classification report ( valid y , pred y ) ) 6 Visualize the tree See an example below fitted tree tree model named steps ' classifier ' class names str ( cls ) for cls in fitted tree classes transformed feature names tree model named steps ' preprocessor ' get feature names out ( ) dot data export graphviz ( fitted tree, out file None, feature names transformed feature names, class names class names, filled True, rounded True, special characters True ) graph graphviz Source ( dot data ) graph render ( Decision Tree , format png graph view ( )

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In Python  1   Set predictors and outcome variables, and partition the data into 6 0   training, 4 0   validation   1  Set predictors and outcome predictors     ' Cleanliness ' , 'Nurse communication, 'Doctor Communication', 'Discharge information', 'Met   Color', 'Automatic', ' CC ' , 'Doors', 'Tax', 'Weight'   outcome   'Rating'   2   Train a tree for classification   3   Run random forest and boosted tree models for classification   4   Which rf variable is the most important  ( e   g   , importances   rf   model named   steps   ' classifier '     feature   importances   )   5   Assess the performance of all models using Sklearn classification   report  Which model is better  See an example below  of how to set up one of the models   classTree   DecisionTreeClassifier ( random   state   0 , max   depth   5 , min   samples   split   5 0 )   tree   model   Pipeline ( steps       ( ' preprocessor ' , preprocessor ) ,   ( ' classifier ' , classTree )     )   tree   model fit ( train   X , train   y )   pred   y   tree   model predict ( valid   X )   print ( '                             DecisionTreeClassifier Classification Report                                         ' )   print ( classification   report ( valid   y , pred   y ) )   6   Visualize the tree  See an example below   fitted   tree   tree   model named   steps   ' classifier '     class   names     str ( cls ) for cls in fitted   tree classes       transformed   feature   names   tree   model named   steps   ' preprocessor '     get   feature   names   out ( )   dot   data   export   graphviz ( fitted   tree, out   file   None,   feature   names   transformed   feature   names,   class   names   class   names,   filled   True, rounded   True,   special   characters   True )   graph   graphviz Source ( dot   data )   graph render (   Decision   Tree , format     png     graph view ( )

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In Python: 1 . Set predictors and outcome variables, and partition the data into 6 0 % training, 4 0 % validation # 1 #Set

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