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I was hoping to hypertune my decisiontree model , however I keep running into this error: TypeError: DecisionTreeClassifier() got an unexpected keyword argument 'criterion'

here what I tried:

def randomsearch():
    data = pd.read_csv('test_data.csv', delimiter=',')
    x = data.iloc[:, :70]
    y = data['class'].values
    param_dist = {"max_depth": [3, None],
              "max_features": randint(1, 9),
              "min_samples_leaf": randint(1, 9),
              "criterion": ["gini", "entropy"]}
    tree = DecisionTreeClassifier()
    tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)
    tree_cv.fit(x,y)
    print("Tuned Decision Tree Parameters: {}".format(tree_cv.best_params_))
    print("Best score is {}".format(tree_cv.best_score_))

I had expected to receive the best parameters for my dataset. This error seems odd any reason why its showing up and how to bypass it?

imports:

import pandas as pd
from sklearn.model_selection import KFold
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report
from random import randint
import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split , cross_val_score , RandomizedSearchCV,GridSearchCV,RepeatedStratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn import preprocessing 
from scipy.stats import loguniform
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.model_selection import LeaveOneOut
import pickle
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  • $\begingroup$ welcome to the site @John Adams. The code you've pasted looks correct to me. Can you add your imports and anything preceding this code as well as the errors from the stack trace? $\endgroup$ Oct 26, 2023 at 16:19
  • $\begingroup$ Issue is my function was named the same as my import $\endgroup$
    – John Adams
    Oct 27, 2023 at 20:56
  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Community Bot
    Oct 29, 2023 at 3:21

1 Answer 1

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The 'criterion' parameter looks good, but the problem seems to be the parameters 'max_features" and "min_samples_leaf" because you are using a single integer not an iterable. Try to add the values inside a list like this:

param_dist = {"max_depth": [3, None],
      "max_features": [randint(1, 9)],
      "min_samples_leaf": [randint(1, 9)],
      "criterion": ["gini", "entropy"]}
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