Scikit-learn using GridSearchCV on DecisionTreeClassifier

Question

I tried to use GridSearchCV on DecisionTreeClassifier, but get the following error: TypeError: unbound method get_params() must be called with DecisionTreeClassifier instance as first argument (got nothing instead)

here's my code:

from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.model_selection import GridSearchCV
from sklearn.cross_validation import  cross_val_score

X, Y = createDataSet(filename)
tree_para = {'criterion':['gini','entropy'],'max_depth':[4,5,6,7,8,9,10,11,12,15,20,30,40,50,70,90,120,150]}
clf = GridSearchCV(DecisionTreeClassifier, tree_para, cv=5)
clf.fit(X, Y)

score 13 · Answer 1 · edited May 19 '20 at 20:41

13

In your call to GridSearchCV method, the first argument should be an instantiated object of the DecisionTreeClassifier instead of the name of the class. It should be

clf = GridSearchCV(DecisionTreeClassifier(), tree_para, cv=5)

Check out the example here for more details.

Hope that helps!

edited May 19 '20 at 20:41

Abhishek

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answered Aug 02 '16 at 00:02

Abhinav Arora

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score 8 · Answer 2 · answered Sep 04 '19 at 02:13

Another aspect regarding the parameters is that grid search can be run with different combination of parameters. The parameters mentioned below would check for different combinations of criterion with max_depth

tree_param = {'criterion':['gini','entropy'],'max_depth':[4,5,6,7,8,9,10,11,12,15,20,30,40,50,70,90,120,150]}

If needed, the grid search can be run over multiple set of parameter candidates:

For example:

tree_param = [{'criterion': ['entropy', 'gini'], 'max_depth': max_depth_range},
              {'min_samples_leaf': min_samples_leaf_range}]

In this case, grid search would be run over two sets of parameters, first with every combination of criterion and max_depth and second, only for all provided values of min_samples_leaf

score 2 · Answer 3 · edited Nov 02 '22 at 15:45

2

You need to add a () after the classifier:

clf = GridSearchCV(DecisionTreeClassifier(), tree_para, cv=5)

edited Nov 02 '22 at 15:45

Community

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answered Dec 21 '17 at 05:52

aghd

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score 2 · Answer 4 · answered Aug 22 '19 at 13:19

Here is the code for decision tree Grid Search

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

score 1 · Answer 5 · answered Jun 01 '17 at 14:56

1

If the problem is still there try to replace :

from sklearn.grid_search import GridSearchCV

with

from sklearn.model_selection import GridSearchCV

It sounds stupid but I had similar problems and I managed to solve them using this tip.

answered Jun 01 '17 at 14:56

seralouk

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Scikit-learn using GridSearchCV on DecisionTreeClassifier

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