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seen Apr 17 at 12:28

Feb
28
awarded  Notable Question
Jan
29
comment How to obtain the class conditional probability when using KNN classifier?
Should we learn the coefficients $B_0$ and $B_1$ ?!
Jan
29
comment How to obtain the class conditional probability when using KNN classifier?
Using that kind of smoothing, we will just get a the same probability values for all classes having $N_i = 0$, so instead of having $P(y_i|x) = 0$, we will get $P(y_i|x) = \frac{s}{K + C \times s} = constant$. Does that change something compared to the 0 probabilities case ?
Jan
29
comment How to obtain the class conditional probability when using KNN classifier?
What do you mean by "using the class probability in the whole data" ? Can you please give more details (by editing your current answer) ? Thanks.
Jan
28
comment How to obtain the class conditional probability when using KNN classifier?
If some classes do not appear in the $k$ neighbourhood of $x$ (e.g. no instance belonging to a given class $y_i$ is among the $k$ nearest instances to $x$); how would you do in this case ?
Jan
28
comment How to obtain the class conditional probability when using KNN classifier?
(1) What is $B_0$ and $B_1$ (how to set them) ? (2) what you call $d$ is the mean distance from $x$ to the $k$ nearest points belonging to a given class $y_i$ (in which case it is $d_i$) ? or is it the mean distance to the instances belonging to the majority class in the $k$ neighbourhood of $x$ ? Please give more details. Thanks.
Jan
28
asked How to obtain the class conditional probability when using KNN classifier?
Jan
14
awarded  Popular Question
Jan
10
comment Assumption to make on data so that we can have an approximation of the final number of representatives
@JacobMick do you mean that we should find the probability p( dist(x,l) > t ) for each x in X and l in L, And that this can only be done through simulation ?
Jan
10
comment Assumption to make on data so that we can have an approximation of the final number of representatives
@NickStauner $x$ is a data point. $c$ is another data point from the list L (just used in the pseudo code to show that d is the distance from x to its nearest point from L). I just want to know if there is assumptions to make on the data (e.g. there distribution) in order to be able to determine the complexity (a function of $t$) in terms of the final size of L. And what is this complexity ?
Dec
20
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Dec
13
comment Assumption to make on data so that we can have an approximation of the final number of representatives
@Glen_b it is just a pseudocode ;)
Dec
12
asked Assumption to make on data so that we can have an approximation of the final number of representatives
Nov
20
accepted Line of best fit (Linear regression) over vertical line
Oct
21
accepted Euclidean distance is usually not good for sparse data?
Oct
5
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Aug
29
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Aug
29
awarded  Popular Question
Jun
17
awarded  Popular Question
Jun
8
asked Probability distribution of distances to micro-cluster centers using particle filtering