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Questions tagged [density-estimation]

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The construction of an estimate, based on observed data, of an unobservable underlying probability density function (pdf).

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How to convert regression into classification?

So I have a regression problem with bunch of features X, and labels in the amount (price $). How can I convert it to classification problem? I have read about convert label from continuous to ...
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how can i interpret kernel density plots from classification?

all, i have a classification problem where i am predicting likelihood of client defaulting on loan. i plotted the predicted probabilities from my model, and then plotted against the label '1' for ...
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50 views

Evaluation of Mixture Density Networks

I have programmed an Mixture Density Network model to a market price. As input I have many numerical and categorical properties. The output of the network is a probability distribution (shape, ...
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14 views

Efficiently estimating the number of clusters in a dataset

Let's assume that I have a high-dimensional dataset and the true number of clusters is quite high (let's say 200 or 300). Are there ways to estimate this number efficiently? I am well aware of the ...
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Using kernel estimation to find similarity/difference between two feature sets for binary classification

I am trying to train a binary classifier using word vectors. I have the tfidf vectors for each sentence in my training set. Before applying binary classification algorithms, I just want to check ...
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Kernel Density Estimation for bimodal distribution with Python

I have a bimodal distribution for the range [-0.1, 0.1] which can be viewed here: I want to train/fit a Kernel Density Estimation (KDE) on the bimodal distribution as shown in the picture and then, ...
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How much can I reduce sample size in a bootstrap used for density estimation?

I have a dataset with 90m rows by 10 numeric/boolean columns. I need to calculate bootstrap distributions for 40 statistics. These statistics are definitely not Gaussian (nor similar to the mean of ...
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Reweighting training data after kernel density estimation?

I have a problem where my training set can have a bit different distribution than my test set, and I'm trying to rectify this by doing a kernel density estimation on my test set, and then applying ...
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Test independence based on Kernel Density Estimation

I am working on a problem where I have a dataset of $X$ is dataset with $(X, Y, T, K)$ four attributes, I'd like to test if $P(X, Y, T)P(K) = P(X, Y, T, K)$, that is if $X, Y, T$ is independent of $K$....
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Simple example of Parzen window (kernel density estimation)

I am confused about the Parzen Window question. Suppose we have two training data points located at 0.5 and 0.7, and we use 0.3 as its rectangle window width. How do we estimate its probability ...