CODEWITHSUNDEEP
pythonscikit-learn
humble
humble

Reputation: 2168

Sklearn : ValueError: Found input variables with inconsistent numbers of samples: [1, 6]

X = [ 1994.  1995.  1996.  1997.  1998.  1999.]
y = [1.2 2.3 3.4 4.5 5.6 6.7]
clf = LinearRegression()
clf.fit(X,y)

This gives the above mentioned error. Both X and y are numpy arrays

How do I remove this error?

I tried the method given here and reshaped X and y by using X.reshape((-1,1)) and y.reshape((-1,1)). However it did not work out.

Upvotes: 4

Views: 38900

Answers (3)

Patrick Muoka
Patrick Muoka

Reputation: 11

I had a similar problem when train test spilt where I had an imbalance variable sample. For my case, I solved it by passing the stratify parameter.

X_train, X_test, y_train, y_test  = train_test_split(
    X, y, test_size=0.3, stratify=y, random_state=42)

Upvotes: 0

Syed Hasan
Syed Hasan

Reputation: 1

import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.cross_validation import train_test_split

df_house = pd.read_csv('CSVFiles/kc_house_data.csv',index_col = 0,engine ='c')

df_house.drop(df_house.columns[[1, 0, 10, 11,12, 13, 14, 15, 16, 17,18]], axis=1, inplace=True)

reg=linear_model.LinearRegression()
df_y=df_house[df_house.columns[1:2]]


df_house.drop(df_house.columns[[6, 7, 8, 5]], axis=1, inplace=True)


x_train, x_test, y_train, y_test=train_test_split(df_house, df_y, test_size=0.1, random_state=7)

print(x_train.shape, y_train.shape)

reg.fit(x_train, x_test)

LinearRegression(copy_x=True, fit_intercept=True, n_jobs=1, normalize=False )

My Shape is :
(19451, 5) (19451, 1)

ValueError: Found input variables with inconsistent numbers of samples: [19451, 2162]

Upvotes: -1

Vivek Kumar
Vivek Kumar

Reputation: 36599

This is working for me fine. Before reshaping make sure that the arrays are numpy arrays.

import numpy as np
from sklearn.linear_model import LinearRegression

X = np.asarray([ 1994.,  1995.,  1996.,  1997.,  1998.,  1999.])
y = np.asarray([1.2, 2.3, 3.4, 4.5, 5.6, 6.7])

clf = LinearRegression()
clf.fit(X.reshape(-1,1),y)


clf.predict([1997])
#Output: array([ 4.5])

clf.predict([2001])
#Output: array([ 8.9])

Upvotes: 4

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