## What is regularization in gradient descent?

Prerequisites: Gradient Descent. Overfitting is a phenomenon that occurs when a Machine Learning model is constraint to training set and not able to perform well on unseen data. Regularization is a technique used to reduce the errors by fitting the function appropriately on the given training set and avoid overfitting.

## How do you explain gradient descent?

Gradient descent is an iterative optimization algorithm for finding the local minimum of a function. To find the local minimum of a function using gradient descent, we must take steps proportional to the negative of the gradient (move away from the gradient) of the function at the current point.

**What is gradient descent in regression?**

Gradient Descent is the process of minimizing a function by following the gradients of the cost function. This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value.

**What is L1 vs L2 regularization?**

The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

### How does regularization affect gradient descent?

ℓ2 regularization makes the objective function more bowl-shaped, and gradient descent requires fewer iterations to converge (17 vs. 100). Note that the solution is different as a consequence of the regularization term.

### Is gradient descent a loss function?

Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function. The loss function describes how well the model will perform given the current set of parameters (weights and biases), and gradient descent is used to find the best set of parameters.

**Where is gradient descent used?**

Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. Gradient descent is simply used in machine learning to find the values of a function’s parameters (coefficients) that minimize a cost function as far as possible.

**How do you solve gradient descent problems?**

Take the gradient of the loss function or in simpler words, take the derivative of the loss function for each parameter in it. Randomly select the initialisation values. Calculate step size by using appropriate learning rate. Repeat from step 3 until an optimal solution is obtained.

#### How does regularization reduce Overfitting?

Regularization is a technique that adds information to a model to prevent the occurrence of overfitting. It is a type of regression that minimizes the coefficient estimates to zero to reduce the capacity (size) of a model. In this context, the reduction of the capacity of a model involves the removal of extra weights.

#### How are the discrete steps of gradient descent regularized?

We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradient Regularization (IGR) and we use backward error analysis to calculate the size of this regularization.

**Why is feature selection important in regularization and gradient descent?**

· Regularization performs feature selection by shrinking the contribution of features. · For L1-regularization, this is accomplished by driving some coefficients to zero. · Feature selection can also be performed by removing features. Why is Feature Selection Important?

**How to perform cross validation in gradient descent?**

#Import the class containing the regression method. #Create an instance of the class. #Fit the instance on the data and then predict the expected value. The RidgeCV class will perform cross validation on a set of values for alpha. #Import the class containing the regression method.

## How to learn a logistic regression function by gradient descent?

2 Logistic Regression Logistic function (or Sigmoid): Learn P(Y|X) directly “ Assume a particular functional form for link function “ Sigmoid applied to a linear function of the input features: Z Features can be discrete or continuous!