506917590 by Unknown

Author:Unknown
Language: eng
Format: epub

Here, θ represents the coefficients of the independent variable Residual Sum of Squares is used to reduce the error existing in the coefficients. Residual Sum of Squares may be defined as follows:

Ridge Regression may be defined as a technique that can be used to analyze multiple regression data that exhibit multicollinearity. Ridge Regression may be defined as follows:

Ridge =

The value of λ is very crucial for our outcome. If the value of λ is zero then there is no affect in the outcome, but if it is equal to infinity then it affects the result and it is not desirable.

Lasso regression helps in shrinking the coefficients to zero and hence, remove them from the model. If there are many features which seem to be irrelevant and can be ignored, then Lasso regression is used. Lasso regression is computationally more intensive. Elastic-net regression is a combination of Lasso regression and ridge regression.

Ridge regression helps in shrinking the coefficients to almost zero but not completely zero.

Consider following code on Lasso regression:

import numpy as np

import pandas as pd

import matplotlib.pyplot

%matplotlib inline

import seaborn as sns

from sklearn.linear_model import Lasso

url 1= 'http://archive.ics.uci.edu/ml/machine-learning-databases/communities/communities.data'

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