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LARS (Least Angle Regression)
2022-08-03 14:15:00 【Full stack programmer webmaster】
Hello everyone, we meet again, I'm your friend Quanstack Jun.
Pros and Cons
LARS is a regression algorithm suitable for high-dimensional data.
Advantages:
- It is especially suitable for the case where the feature dimension n is much higher than the number of samples m.
- The worst-case computational complexity of the algorithm is similar to the least squares method, but its computational speed is almost the same as the forward selection algorithm
- A full path can yield piecewise linear results, which is extremely useful in cross-validation of models
Disadvantages:
- Because the iterative direction of LARS is determined according to the residual of the target, the algorithm is extremely sensitive to the noise of the sample.
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