CODEWITHSUNDEEP
python-3.xmachine-learningscikit-learndecision-tree
Ekin Ersan
Ekin Ersan

Reputation: 65

Decision tree accuracy_score gives "ValueError: Found input variables with inconsistent numbers of samples"

I am trying to create a decision tree with a given data. But for some reason accuracy_score gives

ValueError: Found input variables with inconsistent numbers of samples:

when i split my training data to validation(%20) and training(%80).

Here is how i split my data:

from sklearn.utils import shuffle

from sklearn.model_selection import train_test_split

# stDt shuffled training set

stDt = shuffle(tDt) 

#divide shuffled training set to training and validation set

stDt, vtDt = train_test_split(stDt,train_size=0.8, shuffle=False)

print(tDt.shape)
print(stDt.shape)
print(vtDt.shape)

Here is how i train data:

#attibutes and labels of training set

attributesT =  stDt.values

labelsT = stDt.label


# Train Decision tree classifiers
from sklearn.tree import DecisionTreeClassifier


dtree1 = DecisionTreeClassifier(min_samples_split = 1.0)

dtree2 = DecisionTreeClassifier(min_samples_split = 3)

dtree3 = DecisionTreeClassifier(min_samples_split = 5)



fited1 = dtree1.fit(attributesT,labelsT)

fited2 = dtree2.fit(attributesT,labelsT)

fited3 = dtree3.fit(attributesT,labelsT)

Here is test and accuracy score part:

from sklearn.metrics import accuracy_score

ret1 = fited1.predict(stDt)

ret2 = fited2.predict(stDt)

ret3 = fited3.predict(stDt)

print(accuracy_score(vtDt.label,ret1))

Upvotes: 2

Views: 2634

Answers (1)

desertnaut
desertnaut

Reputation: 60400

The error you get is expected, since you are trying to compare the predictions produced from your training set (ret1 = fited1.predict(stDt)) to the labels of your validation set (vtDt.label).

Here is the correct way to get both your training & validation accuracy for your fitted1 model (similarly for the others):

# predictions on the training set:
ret1 = fitted1.predict(stDt)

# training accuracy:
accuracy_score(stDt.label,ret1)

# predictions on the validation set:
pred1 = fitted1.predict(vtDt)

# validation accuracy:
accuracy_score(vtDt.label,pred1)

Upvotes: 3

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