Why should re-sampling change the value of model's coefficients? I have the code below in python to create LinearRegression model. When I train the model with re-sampled data, I get different values for its coefficients. I can't understand why that happens. Can you help me in this please? 

[Update]


I assume that resampling is the same as shuffling. And that means the order of the data is changed but not the data itself.
In the use case presented, the number of rows are are the same as I inspected it and as I understand the order of the data is changed.


Thanks!

from sklearn.linear_model import LinearRegression
from sklearn.utils import resample

model = LinearRegression(fit_intercept=False)

model.fit(X, y)
print('model.coef_',model.coef_)

model.fit(*resample(X, y))
print('model.coef_',model.coef_)

model.fit(*resample(X, y))
print('model.coef_',model.coef_)
In each sampling, your data is going to be different from the previous sampling. For each sampling, you are going to find the best line which describes your sample with the least error value. Consequently, for each sample you are going to find a model which may be different due to the fact that it reduces the cost for each sample.