## How do you choose Alpha in elastic net?

Simply put, if you plug in 0 for alpha, the penalty function reduces to the L1 (ridge) term and if we set alpha to 1 we get the L2 (lasso) term. Therefore we can choose an alpha value between 0 and 1 to optimize the elastic net. Effectively this will shrink some coefficients and set some to 0 for sparse selection.

**What is lambda in Lasso?**

Source: Standford. Here lambda is the penalty coefficient and it’s free to take any allowed number while alpha is selected based on the model you want to try . So if we take alpha = 0, it will become Ridge and alpha = 1 is LASSO and anything between 0–1 is Elastic net.

**Can I use Lasso for classification?**

You can use the Lasso or elastic net regularization for generalized linear model regression which can be used for classification problems. Here data is the data matrix with rows as observations and columns as features. group is the labels.

### Which is better ridge or lasso?

Lasso tends to do well if there are a small number of significant parameters and the others are close to zero (ergo: when only a few predictors actually influence the response). Ridge works well if there are many large parameters of about the same value (ergo: when most predictors impact the response).

**What is lambda in regression?**

In ridge regression, we add a penalty by way of a tuning parameter called lambda which is chosen using cross validation. The idea is to make the fit small by making the residual sum or squares small plus adding a shrinkage penalty.

**What is lambda in linear regression?**

When we have a high degree linear polynomial that is used to fit a set of points in a linear regression setup, to prevent overfitting, we use regularization, and we include a lambda parameter in the cost function. This lambda is then used to update the theta parameters in the gradient descent algorithm.

## What is the purpose of ridge regression?

Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.

**How is OLS calculated?**

OLS: Ordinary Least Square MethodSet a difference between dependent variable and its estimation:Square the difference:Take summation for all data.To get the parameters that make the sum of square difference become minimum, take partial derivative for each parameter and equate it with zero,

**Why is OLS the best estimator?**

In this article, the properties of OLS estimators were discussed because it is the most widely used estimation technique. OLS estimators are BLUE (i.e. they are linear, unbiased and have the least variance among the class of all linear and unbiased estimators).

### What are the OLS assumptions?

Why You Should Care About the Classical OLS Assumptions In a nutshell, your linear model should produce residuals that have a mean of zero, have a constant variance, and are not correlated with themselves or other variables.