what's so special about 80% threshold for PCA variance ratio?
Why is 80% of the PCA.explained_variance_ratio_ seem like a reasonable threshold? What can one say about the number of components required to explain 80% of the variance?
According to the PCA documentation,
auto:
the solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards.
Ok, I'm not sure if I'm even making sense, but it seems like 80% is a good threshold, but why? I tried looking this up, but it didn't amount to much.
python pca
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
add a comment |
Why is 80% of the PCA.explained_variance_ratio_ seem like a reasonable threshold? What can one say about the number of components required to explain 80% of the variance?
According to the PCA documentation,
auto:
the solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards.
Ok, I'm not sure if I'm even making sense, but it seems like 80% is a good threshold, but why? I tried looking this up, but it didn't amount to much.
python pca
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
add a comment |
Why is 80% of the PCA.explained_variance_ratio_ seem like a reasonable threshold? What can one say about the number of components required to explain 80% of the variance?
According to the PCA documentation,
auto:
the solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards.
Ok, I'm not sure if I'm even making sense, but it seems like 80% is a good threshold, but why? I tried looking this up, but it didn't amount to much.
python pca
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
Why is 80% of the PCA.explained_variance_ratio_ seem like a reasonable threshold? What can one say about the number of components required to explain 80% of the variance?
According to the PCA documentation,
auto:
the solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards.
Ok, I'm not sure if I'm even making sense, but it seems like 80% is a good threshold, but why? I tried looking this up, but it didn't amount to much.
python pca
python pca
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
edited Nov 18 '18 at 15:55
Wai Ha Lee
5,862123764
5,862123764
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
asked Nov 18 '18 at 15:34
thefalsehumanthefalsehuman
405
405
add a comment |
add a comment |
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