Dalal via scikit-learn
Sent: Tuesday, October 4, 2016 6:44 AM
To: Scikit-learn user and developer mailing list
Cc: Ibrahim Dalal
Subject: Re: [scikit-learn] Random Forest with Bootstrapping
⚠ EXT MSG:
Hi,
So why is using a bootstrap sample of size n better than just a random set of
size 0.62
Hi,
So why is using a bootstrap sample of size n better than just a random set
of size 0.62*n in Random Forest?
Thanks
On Tue, Oct 4, 2016 at 1:58 AM, Sebastian Raschka
wrote:
> Originally, it was this technique was used to estimate a sampling
> distribution. Think of the drawing with replacem
Originally, it was this technique was used to estimate a sampling distribution.
Think of the drawing with replacement as work-around for generating *new* data
from a population that is simulated by this repeated sampling from the given
dataset with replacement.
For more details, I’d recommend
So what is the point of having duplicate entries in your training set? This
seems just a pure overhead. Sorry but you will again have to help me here.
On Tue, Oct 4, 2016 at 1:29 AM, Sebastian Raschka
wrote:
> > Hi,
> >
> > That helped a lot. Thank you very much. I have one more (silly?) doubt
>
> Hi,
>
> That helped a lot. Thank you very much. I have one more (silly?) doubt though.
>
> Won't an n-sized bootstrapped sample have repeated entries? Say we have an
> original dataset of size 100. A bootstrap sample (say, B) of size 100 is
> drawn from this set. Since 32 of the original samp
Hi,
That helped a lot. Thank you very much. I have one more (silly?) doubt
though.
Won't an n-sized bootstrapped sample have repeated entries? Say we have an
original dataset of size 100. A bootstrap sample (say, B) of size 100 is
drawn from this set. Since 32 of the original samples are left out
Or maybe more intuitively, you can visualize this asymptotic behavior e.g., via
import matplotlib.pyplot as plt
vs = []
for n in range(5, 201, 5):
v = 1 - (1. - 1./n)**n
vs.append(v)
plt.plot([n for n in range(5, 201, 5)], vs, marker='o',
markersize=6,
alpha=0.5,)
Say the probability that a given sample from a dataset of size n is *not* drawn
as a bootstrap sample is
P(not_chosen) = (1 - 1\n)^n
Since you have a 1/n chance to draw a particular sample (since bootstrapping
involves drawing with replacement), which you repeat n times to get a n-sized
bootst
Hi,
Thank you for the reply. Please bear with me for a while.
>From where did this number, 0.632, come? I have no background in statistics
(which appears to be the case here!). Or let me rephrase my query: what is
this bootstrap sampling all about? Searched the web, but didn't get
satisfactory re
Hi,
>From docs
http://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html
:
The RandomForestClassifier is trained using bootstrap aggregation, where
each new tree is fit from a bootstrap sample of the training observations
z_i = (x_i, y_i). The out-of-bag (OOB) error is the aver
> From whatever little knowledge I gained last night about Random Forests, each
> tree is trained with a sub-sample of original dataset (usually with
> replacement)?.
Yes, that should be correct!
> Now, what I am not able to understand is - if entire dataset is used to train
> each of the tree
Dear Developers,
>From whatever little knowledge I gained last night about Random Forests,
each tree is trained with a sub-sample of original dataset (usually with
replacement)?.
(Note: Please do correct me if I am not making any sense.)
RandomForestClassifier has an option of 'bootstrap'. The A
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