Using ID3 Estimator for Decision Trees

[student6306]

Hi, I got stuck on question d and onwards some help would be very much appreciated.
Following is the code I have so far but on question d I'm totally lost :(

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

%%capture --no-display # hack omwille van bug in Id3Estimator import six import sys from sklearn import tree from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt   sys.modules['sklearn.externals.six'] = six   #todo B  We are now wondering on the basis of which criteria the teacher has given his scores To do this, set up a decision tree for the score with ID3Estimator. from IPython.core.display_functions import display   import pandas as pd import graphviz from id3 import Id3Estimator, export_graphviz, export_text   scores = pd.read_csv("studentsScores.csv") model = Id3Estimator()   # X = attributes; y = target X = scores.drop(columns='score', axis=1).to_numpy() # X = simpsons.drop(['name', 'gender'], axis=1).values.tolist() y = scores['score'].to_numpy() # y = simpsons['gender'].values.tolist()   # build model model.fit(X, y)   # plot model model_tree = export_graphviz(model.tree_,                              feature_names=scores.drop('score', axis=1).columns) display(graphviz.Source(model_tree.dot_tree)) # todo c. Which subjects does the teacher teach? # Answer:Tree structure uses only subject4 and subject1. # So the teacher probably gives these subjects.   # todo d We are dividing the points into categories: not successful (0-9), satisfactory (10-13), honors (14-15), highest honors (16-20). Try to classify the scores as mentioned #Divide the subject scores into categories as mentioned above:   bins = [-1, 9, 13, 15, 21] labels = ["not successful",           "satisfactory",           "honors",           "highest honors"]   subject_columns = scores.columns[:-1] for subject in subject_columns:#Exclude the last column 'score'     scores[subject] = pd.cut(scores[subject], bins=bins, labels=labels)   #Important: By setting right=False, the intervals will be left-inclusive and right-exclusive, meaning that the right end of each interval is not included. This ensures that scores of 0 and 20 fall within the appropriate intervals. import sys sys.modules['sklearn.externals.six'] = six from id3 import Id3Estimator, export_graphviz, export_text model = Id3Estimator() # X = features, y = target   X = (scores.drop(columns=['score'],axis=1)).values.tolist() y = scores['score'].values.tolist() model.fit(X,y) print(export_text(model.tree_, feature_names=scores.drop(['score'], axis=1).columns))

Error:
I don't get any errors when I execute the code but the ID3 Estimator doesn't show anything as it should for the question E

Output:
As the output all I got so far is the tree generated using the ID3Estimator which was the answer to the question B and I also attached that tree in the attachments

[Larz60+]

can you post a sample 'studentsScores.csv' file (small)?

[student6306]

can you post a sample 'studentsScores.csv' file (small)?

Sample below:
subject1,"subject2","subject3","subject4","subject5","subject6","subject7","subject8","subject9","score"
13,9,17,2,17,1,0,14,18,"average"
10,13,5,20,20,14,4,2,10,"good"
13,4,2,19,3,9,16,13,7,"good"
8,3,17,7,16,10,15,9,8,"bad"
1,12,13,2,9,10,2,2,13,"bad"
17,2,19,1,13,14,5,5,2,"average"
3,19,7,13,4,5,5,15,9,"bad"
18,18,13,16,17,5,7,0,5,"good"
3,3,8,11,16,5,3,19,12,"bad"