## What is the difference between norm 1 and norm 2?

Specifically, you learned: The L1 norm that is calculated as the sum of the absolute values of the vector. The L2 norm that is calculated as the square root of the sum of the squared vector values.

### Should I use L1 or L2 norm?

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.

**What is a 1-norm?**

The 1-norm is simply the sum of the absolute values of the columns.

**What does L1 norm tell you?**

L1 Norm is the sum of the magnitudes of the vectors in a space. It is the most natural way of measure distance between vectors, that is the sum of absolute difference of the components of the vectors. In this norm, all the components of the vector are weighted equally.

## Why is L1 robust than L2?

Robustness: L1 > L2 The L1 norm is more robust than the L2 norm, for fairly obvious reasons: the L2 norm squares values, so it increases the cost of outliers exponentially; the L1 norm only takes the absolute value, so it considers them linearly.

### What are the advantages of L1 over L2 normalization?

Advantages of L1 over L2 norm (explanation on Quora) This means the L1 norm performs feature selection and you can delete all features where the coefficient is 0. A reduction of the dimensions is useful in almost all cases. The L1 norm optimizes the median. Therefore the L1 norm is not sensitive to outliers.

**What’s the difference between L1 and L2 regularization and why would you use each?**

L1 regularization gives output in binary weights from 0 to 1 for the model’s features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.

**What is a L2 norm?**

The -norm (also written ” -norm”) is a vector norm defined for a complex vector.

## What are some examples of norms?

Examples include:

- Acknowledge others in the elevator with a simple nod or say hi.
- Stand facing the front.
- Never push extra buttons, only the one for your floor.
- Never stand right by someone if you are the only two people on board.
- Do not act obnoxiously on the elevator.

### What is the difference between L1 L2 regularization?

The differences between L1 and L2 regularization: L1 regularization penalizes the sum of absolute values of the weights, whereas L2 regularization penalizes the sum of squares of the weights. The L1 regularization solution is sparse. The L2 regularization solution is non-sparse.

**Why is L2 norm more stable than L1 norm?**

L2-norm is more stable in small adjustment of a data point is because L2-norm is continuous. L1 has absolute value which makes it a non-differenciable piecewise function.