# Linked Questions

2answers
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 ...
2answers
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. ...
2answers
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 ...
2answers
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 (...
2answers
2k views

### Full-Rank design matrix from overdetermined linear model

I'm trying to create a full-rank design matrix X for a randomized block design model starting from something like the example from page 3/8 of this paper . It's been suggested that I can go about ...
2answers
1k views

### How is the linear regression optimize in R and Python?

I am currently working a lot with R and Python. I am not able to access the C code the the R function lm_fit. I am wondering how is the linear regression optimize in R and python ? I am pretty sure ...
1answer
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 ...
1answer
962 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 ...
1answer
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 ...
1answer
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 ...
1answer
56 views

### Is stochastic gradient descent unable to learn more complex models which batch gradient descent can learn?

Here's a toy dataset. ...
1answer
70 views

### The best line fit can be found analytically by the least squares method. So can we say that linear regression (least squares) has an optimizer?

The best line fit can be found analytically by the least squares method. So can we say that linear regression (least squares) has an optimizer? For example, for logistic regression I can use an ...
0answers
34 views

### Various Methods to Calculate Linear Regression [duplicate]

I have just started learning Machine Learning and one of the very first topics that I have encountered in this venture is Simple Linear Regression. From Andrew Ng's course, I have learned to perform ...
0answers
56 views

### Linear least squares algorithms

I have stumbled across these two questions and accepted answers: (1) Do we need gradient descent to find the coefficients of a linear regression model? (2) Why use gradient descent for linear ...
0answers
404 views

### Linear Regression using Matrices vs. Covariance(x,y) / Var(x)

From taking some online linear regression courses (Stanford Machine Learning and John Hopkins Linear Regression) it seems that there are at least three ways of finding a the coefficients of a linear ...

15 30 50 per page