# Lasso coefficient for some features is higher than Linear Regression Coefficient

I'm using Lasso Regularization to avoid overfitting & multicollinearity between two features (X1 and X2), nowing that I have 14 independent features. I got some good results for some features, Lasso was able to reduce the coefficient to 0, but for other features the linear regression coefficient was less than Lasso (same thing for Ridge).

lr = LinearRegression()
lr.fit(X, Y)
lr_coeff = lr.coef_
lr_intercept = lr.intercept_

lasso = Lasso(alpha=10)
lasso.fit(X, Y)
lasso_coeff = lasso.coef_
lasso_intercept = lasso.intercept_


Result:

    lr_coeff  lr_intercept  lasso_coeff  lasso_intercept
0   0.968567      16.01858     0.000000       103.471224
1   1.743420      16.01858     1.730920       103.471224
2   5.221518      16.01858     3.931450       103.471224
3   4.769328      16.01858     3.186003       103.471224
4   6.341612      16.01858     4.265931       103.471224
5   2.272504      16.01858     1.277541       103.471224
6   3.104016      16.01858     1.648253       103.471224
7   1.418943      16.01858     0.667189       103.471224
8   1.144834      16.01858     0.000000       103.471224
9   0.138457      16.01858     0.000000       103.471224
10  1.272995      16.01858     0.693323       103.471224
11  0.188450      16.01858     0.503958       103.471224
12 -2.334245      16.01858    -0.167953       103.471224
13 -0.475823      16.01858     0.124608       103.471224
14  0.489548      16.01858     0.512034       103.471224


Sincerely,

• To be honest I think it is perfectly fine. Commented Feb 14, 2022 at 10:38
• What is the question? Commented Feb 14, 2022 at 16:32