Questions tagged [normal-equation]
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Loss-value of normal equation vs gradient descent
My question is if gradient descent can give a better aproximation than normal equation in Python?
for the Loss function, I wrote
...
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Is it usual for Scikit learn's standard scaler to cause non-invertibility?
For example, I am trying to perform linear regression on the following set of data
Data examples:
$X = [[1, 20], [3, 40], [5, 60]]$ (each row is an example, there are three examples, each with a ...
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Normal equation for linear regression is illogical
Currently I'm taking Andrew Ng's course. He gives a following formula to find solution for linear regression analytically:
$θ = (X^T * X)^{-1} * X^T * у$
He doesn't explain it so I searched for it and ...
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Gradient Descent or Normal Equation?
Suppose you have a dataset with m = 50 examples and n = 15 features for each example. You want to use multivariate linear regression to fit the parameters theta to our data. Should you prefer gradient ...
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What is the differences between normal equation and gradient descent for polynomial regression
I'm new to machine learning and willing to study and work with machine learning. It just that I still don't get to understand the benefits of using the normal equation in some occasion in comparison ...
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Linear Regression - finding thetha using Normal equation
This is to find thetha which will give minimum cost function. Why is the x0 column required? why cant we assign size as x0? why do we need the feature count to be n+1?
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How does "linear algebraic" weight training function work?
This answer shows that linear and polynomial function weights can be trained using this matrix operation:
$w = (X^TX)^{-1}X^Ty$
Therefore, algorithms such as gradient descent are not necessary for ...
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Why adding combinations of features would increase performance of linear SVM?
I have a dataset of ~5000 elements represented by vectors composed by ~30 binary values (0 or 1)
on which I am performing binary classification with SVM with linear kernel (I use the Scikit learn lib)...
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Polynomial regression vs. multilayer perceptron [closed]
Polynomial regression and multilayer perceptrons have different structures and different learning procedures. What are these two algorithms pros and cons? Are there some situations where one should ...
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