- What is feature selection in ML?
- Why is l2 better than l1?
- How does lasso work?
- How does l2 regularization prevent Overfitting?
- Is lasso convex?
- Which is better ridge or lasso?
- Why does the lasso give zero coefficients?
- Can we use l2 regularization for feature selection?
- Is PCA a feature selection?
- Why do we use Lasso?
- Is lasso l1 or l2?
- How do you do feature selection in machine learning?
- How do you select a linear regression feature?
- Why do we use Lasso regression?
- Can I use Lasso for classification?

## What is feature selection in ML?

In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction..

## Why is l2 better than l1?

From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero. L2, on the other hand, is useful when you have collinear/codependent features.

## How does lasso work?

What is Lasso Regression? Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters).

## How does l2 regularization prevent Overfitting?

That’s the set of parameters. In short, Regularization in machine learning is the process of regularizing the parameters that constrain, regularizes, or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, avoiding the risk of Overfitting.

## Is lasso convex?

The lasso solution is unique when rank(X) = p, because the criterion is strictly convex.

## 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).

## Why does the lasso give zero coefficients?

The lasso performs shrinkage so that there are “corners” in the constraint, which in two dimensions corresponds to a diamond. If the sum of squares “hits” one of these corners, then the coefficient corresponding to the axis is shrunk to zero.

## Can we use l2 regularization for feature selection?

So while L2 regularization does not perform feature selection the same way as L1 does, it is more useful for feature *interpretation*: a predictive feature will get a non-zero coefficient, which is often not the case with L1.

## Is PCA a feature selection?

The only way PCA is a valid method of feature selection is if the most important variables are the ones that happen to have the most variation in them . However this is usually not true. … Once you’ve completed PCA, you now have uncorrelated variables that are a linear combination of the old variables.

## Why do we use Lasso?

In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.

## Is lasso l1 or l2?

A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function.

## How do you do feature selection in machine learning?

Feature Selection: Select a subset of input features from the dataset. Unsupervised: Do not use the target variable (e.g. remove redundant variables). Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features.

## How do you select a linear regression feature?

In the Stepwise regression technique, we start fitting the model with each individual predictor and see which one has the lowest p-value. Then pick that variable and then fit the model using two variable one which we already selected in the previous step and taking one by one all remaining ones.

## Why do we use Lasso regression?

The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero.

## 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.