30k views

### What algorithm is used in linear regression?

I usually hear about "ordinary least squares". Is that the most widely used algorithm used for linear regression? Are there reasons to use a different one?
11k views

### Do we need gradient descent to find the coefficients of a linear regression model?

I was trying to learn machine learning using the Coursera material. In this lecture, Andrew Ng uses gradient descent algorithm to find the coefficients of the linear regression model that will ...
13k views

### How could stochastic gradient descent save time compared to standard gradient descent?

Standard Gradient Descent would compute gradient for the entire training dataset. ...
7k views

### Why not use the “normal equations” to find simple least squares coefficients?

I saw this list here and couldn't believe there were so many ways to solve least squares. The "normal equations" on Wikipedia seemed to be a fairly straight forward way:  {\displaystyle {\begin{...
17k views

### How to choose the right optimization algorithm?

I need to find the minimum of a function. Reading the docs at http://docs.scipy.org/doc/scipy/reference/optimize.html I see that there are several algorithms that do the same thing, i.e. find the ...
11k views

### Why is gradient descent required?

When we can differentiate the cost function and find parameters by solving equations obtained through partial differentiation with respect to every parameter and find out where the cost function is ...
4k views

### Why is optimisation solved with gradient descent rather than with an analytical solution? [duplicate]

I'm trying to understand why, when trying to minimise an objective function, gradient descent is often used, rather than setting the gradient of the error to zero, and solving it analytically. In ...
5k views

### What is the purpose of a neural network activation function?

What is the purpose of a neural network having a non-linear activation function? Is it correct to say that the non-linear activation function's main purpose is to allow the neural network's decision ...
964 views

### Difference Between Linear Regression in Machine Learning and Statistical Model

I had the understanding that the major difference between machine learning and statistical model is, the later "assumes" certain type of distribution of data & based on that different model ...
4k views

### Linear regression for large dataset

If the dataset is too large to be entirely loaded into memory, how can we do linear regression with the dataset?
46 views

### Significant Difference in prediction when using library and coding from scratch in Multiple Linear Regression [duplicate]

I have been trying to implement multiple linear regression from scratch after implementing it using sklearn. The values predicted using sklearn is very accurate whereas the values predicted by the ...
1k views

### Which ML Algorithms are affected by dummy variable trap?

My understanding is that regression models are affected by the dummy variable trap. What about other machine learning algorithms e.g. linear svm, logistic regression? Also, if an algorithm is not ...
2k views

### sklearn Linear Regression vs Batch Gradient Descent

tldr: Why would sklearn LinearRegression give a different result than gradient descent? My understanding is that LinearRegression is computing the closed form solution for linear regression (...