Linked Questions

0
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0answers
27 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 ...
45
votes
6answers
29k 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?
36
votes
2answers
10k 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 ...
11
votes
4answers
9k 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 ...
17
votes
3answers
6k 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{...
19
votes
1answer
11k views

How could stochastic gradient descent save time comparing to standard gradient descent?

Standard Gradient Descent would compute gradient for the entire training dataset. ...
18
votes
2answers
15k 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 ...
7
votes
4answers
4k 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 ...
4
votes
3answers
3k 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?
7
votes
1answer
3k 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 ...
1
vote
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 ...
7
votes
1answer
878 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 ...
2
votes
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 (...
0
votes
2answers
995 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 ...
2
votes
1answer
749 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 ...

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