Contents
- 1 Can we use gradient descent for logistic regression?
- 2 How does gradient descent work in logistic regression?
- 3 What is the loss function used in Logistic Regression to find the best fit line?
- 4 Which is the best algorithm for stochastic gradient descent?
- 5 What are the penalties for stochastic gradient descent?
- 6 How to classify a class by gradient descent?
Can we use gradient descent for logistic regression?
Surprisingly, the update rule is the same as the one derived by using the sum of the squared errors in linear regression. As a result, we can use the same gradient descent formula for logistic regression as well.
How does gradient descent work in logistic 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 gradient in logistic regression?
The objective of gradient descent is to find out optimal parameters that result in optimising a given function. In the Logistic Regression algorithm, the optimal parameters θ are found by minimising the following loss function: Loss function of Logistic Regression (m: number of training examples)
What is the loss function used in Logistic Regression to find the best fit line?
Log Loss
Log Loss is the loss function for logistic regression. Logistic regression is widely used by many practitioners.
Which is the best algorithm for stochastic gradient descent?
For classification with a logistic loss, another variant of SGD with an averaging strategy is available with Stochastic Average Gradient (SAG) algorithm, available as a solver in LogisticRegression. 1.5.2. Regression ¶
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!
What are the penalties for stochastic gradient descent?
SGD supports the following penalties: penalty=”l2″: L2 norm penalty on coef_. penalty=”l1″: L1 norm penalty on coef_. penalty=”elasticnet”: Convex combination of L2 and L1; (1 – l1_ratio) * L2 + l1_ratio * L1. The default setting is penalty=”l2″. The L1 penalty leads to sparse solutions, driving most coefficients to zero.
How to classify a class by gradient descent?
Classification Learn: h:X Y “ X– features “ Y – target classes Conditional probability: P(Y|X) Suppose you know P(Y|X) exactly, how should you classify?