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Today I am trying build ensemble model. Where I am working with iris dataset. In my model I am using LogisticRegression, KNeighborsClassifier, RandomForestClassifier. But when I am going run the program I get ValueError: Found input variables with inconsistent numbers of samples: [10, 150] error. Below I am giving my code:

df = pd.read_csv('/kaggle/input/iriscsv/Iris.csv')
df.head()
df

output --->

enter image description here

Then I am deleting id collumn from this dataset

df = df.iloc[:, 1:]

After this I am used LabelEncoder on Species column

df['Species'] = encoder.fit_transform(df['Species'])
import seaborn as sns 
sns.pairplot(df, hue = 'Species')

Output

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score


clf1 = LogisticRegression()
clf2 = RandomForestClassifier()
clf3 = KNeighborsClassifier()

estimators = [('lr',clf1),('rf',clf2),('knn',clf3)]


for estimator in estimators:
    x = cross_val_score(estimator[1],x,y,cv=10,scoring='accuracy')
    print(estimator[0],np.round(np.mean(x),2))

After running the last estimator I am getting these error.

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1 Answer 1

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In your 2nd last line, you are overwriting the variable x, which previously held your input X data.

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  • $\begingroup$ This does not provide an answer to the question. To critique or request clarification from an author, leave a comment below their post. - From Review $\endgroup$
    – lcrmorin
    Sep 7, 2022 at 11:44
  • $\begingroup$ @Icrmorin Did you read the question, and subsequently read my answer - or did you just see a single sentence and assume? The question is: "Why am I getting ValueError". My answer is that the 2nd last line, is being overwirtten, causing ValueError. A very direct answer. If you have a better one go ahead and answer it. $\endgroup$
    – GooJ
    Sep 7, 2022 at 20:21
  • $\begingroup$ I will conceed there is merit to asking if this should be on stack overflow rather than data science. $\endgroup$
    – GooJ
    Sep 7, 2022 at 20:22
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    $\begingroup$ Yeah the automatic wording is a bit bad. For this kind of question I would usually vote to close and provide an answer in comment. I am sorry if this gave you the impression you are at fault here. $\endgroup$
    – lcrmorin
    Sep 8, 2022 at 15:25

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