What's the difference between dummy variable and one-hot encoding?
In fact, there is no difference in the effect of the two approaches (rather wordings) on your regression.
In either case, you have to make sure that one of your dummies is left out (i.e. serves as base assumption) to avoid perfect multicollinearity among the set. 
For instance, if you want to take the weekday of an observation into account, you only use 6 (not 7) dummies assuming the one left out to be the base variable. When using one-hot encoding, your weekday variable is present as a categorical value in one single column, effectively having the regression use the first of its values as the base.
I'm making features for a machine learning model. I'm confused with dummy variable and one-hot encoding.For a instance,a category variable 'week' range 1-7.When using one-hot encoding, encode week = 1 as 1,000,000,week = 2 is 0,100,000... .But I can also make a dummy variable 'week_v',and in this way, I must set a 
hidden variable which means base variable,and feature week_v = 1 is 100,000,week_v = 2 is 010,000... and 
does not appear week_v = 7.So what's the difference between them? I'm using logistic model and then I'll try gbdt.