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如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注IT技能提升
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注IT技能提升
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注IT技能提升
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注IT技能提升
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注C++内核技术
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注C++内核技术
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网移动版 - 专注C++内核技术
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网 - 专注C/C++及内核技术
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
如何选择机器学习算法 - 大数据 & AI - 清泛网 - 专注C/C++及内核技术
... work well even if you’re data isn’t linearly separable in the base feature space. Especially popular in text classification problems where very high-dimensional spaces are the norm. Memory-intensive, hard to interpret, and kind of annoying to run and tune, though, so I think random fore...
Cross-platform way of getting temp directory in Python
...
The simplest way, based on @nosklo's comment and answer:
import tempfile
tmp = tempfile.mkdtemp()
But if you want to manually control the creation of the directories:
import os
from tempfile import gettempdir
tmp = os.path.join(gettempdir(...