Questions tagged [algorithms]
An unambiguous list of computational steps involved in finding a solution to a class of problems.
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Generating samples from a distribution while ensuring sample mean equals distribution's mean? [closed]
I am working with a software that for a given distribution and sample size $N$, it produces $N$ data points with a guarantee that the sample mean of these points is equal to the mean of the assumed ...
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How do find the best arm in a multi-armed bandit when exploitation is unimportant?
I have a problem similar to the 'Bernoulli bandit' problem in the exploration-exploitation paradigm, but without the exploitation element.
In particular, I have many levers that I can pull and each ...
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What unequal probability sampling methods exist for weights which are extremely different from each other?
Suppose I have $N$ samples, each having a relative weight of $w_1, \ldots, w_N$. Assume that the largest weight is $10,000$, while the smallest weight is just $1$.
Suppose I wanted to get $m\leq N$ ...
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Check if growth rate is worse than quadratic?
Let's say I have collected a dataset for estimating algorithmic complexity:
x
t
1
1
2
4
3
9
4
16
where x is the input size and t is the elapsed time. This may be ...
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What is an efficient algorithm for finding the minimum of a parabola-shaped function? [closed]
I have a continuous function f(x) that is bounded on the interval (0, N), where N is a large positive integer (~10,000,000). The function is shaped like an upwards-facing parabola, however, it is ...
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Clustering real estate in R [closed]
guys
I have started working with spatial data in r, more precisely, data about real estate. My data is both "data.frame" and "sf", I also have variables related to price, surface, ...
Dulipip Truman Miller
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Why xgboost BDT model constructed with histogram method depends on the training data ordering?
I was using Python xgboost to train some models (with binary logistic) on some data (50k in total) and I used the histogram tree method for the training (tree_method="hist").
By accidence I ...
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Reorder dataset to achive LSE between two data sets
Assume I have two datasets, each one containing 5000 samples, and each sample has three dimensions. I am looking for a way to "reorder" the samples in one (or probably both) dataset such ...
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Why do we need a binary tree for this way of computing the two-sample Kolmogorov-Smirnov statistic?
In this paper, Detecting Change in Data Streams, section 5 Algorithms, the authors show a way to compute the two-sample Kolmogorov-Smirnov statistic
over intervals and initial segments in $O(log(m_{1}+...
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Expectation Maximization: How to perform "E" step in coin flipping example? [duplicate]
I recently read this primer that does a great job of explaining the principles of the Expectation-Maximization (EM) "algorithm."
However, I'm confused about how they calculated the ...
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Why a fully factorised approximation in message passing algorithms?
I would like to understand how to do Bayesian inference in a Bayesian network that contains a mixture of discrete and continuous random variables.
There are two algorithms that seem like they do ...
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Formal approach to message passing algorithms
I'm trying to understand message passing algorithms, especially for the specific application of performing conditioning in a Bayesian network. My question is wether there is a mathematically precise ...
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How to fix the tree structure for a tree-based algorithm?
Background
Some of our BI analysts and most of our managers are interested in making explainable predictions. One of our colleagues proposed an approach based on individual tree leaves from a tree-...
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Combinations from different sets with weightings
Imagine the following scenario:
I want to create 1000 unique combinations of clothing. The combinations would include the following categories: hats, shirts, shorts, socks and shoes. Each combination ...
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Robust distance weighted mean
Given a data sample $\{x_i\}_1^n$, instead of hard omitting outliers by e.g. trimming, one can form a weighted average where we soft penalize observations out in the tails.
\begin{align}
\mu = \frac{...
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no free lunch theorem version 2
Let data-generating function $f$ be fixed, $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $A$ is set of all possible deterministic learning algorithms and $h:X\rightarrow Y$ is a classifier trained by the ...
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how to implement a next position predictor machine learning model? [closed]
I am trying to implement next position predictor base on machine Learning algorithms.I want to know how can i start building my model ?
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No free lunch theorem proof
Assume that learning algorithm $A$ is fixed. Let $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $F$ is set of a data-generating functions(meaning $f \in F$ then $f(x_i) = y_i$ and that functions in $F$ are ...
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DoE for optimization / control approach?
I'm wondering whether a DoE approach could somehow be used as kind of an optimization algorithm?
One of my current tasks is to find a set of five parameter which max a sixth one (see here for more: ...
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clustering sets of numbers
I have some experimental measurements which come in sets $S_i$. Each $S_i$ contains some floating point numbers $s_{ij}$. I think the $S_i$ form clusters with similar contents. How do I assign the $...
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Algorithm for sampling fixed number of samples from a finite population
I'm looking for an algorithm that would do the following:
Imagine that you need to sample uniformly at random and without replacement $k$ elements from a pool of $n$ elements. The catch is that $n$ is ...
Tim♦
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For what do we need the expected value $\hat{\mathop{\mathbb{E}}}_t$ operation in PPO?
Given the PPO algorithm from reinforcement learning.
$L^{CLIP}(\theta)=\hat{\mathop{\mathbb{E}}}_t \left[\min(r_t(\theta)\hat{A_t}, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon)\hat{A}_t)\right]$
I ...
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Evaluating randomized algorithms on randomized problem instances
Suppose we want to compare the performances of two algorithms — call them A and B — on a problem X. In particular, suppose that we want to evaluate the algorithms on random instances of X drawn from ...
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Why PC algorithm is order dependent?
I am studying constraint-based causal discovery algorithms like PC.
From a paper, I learned that PC algorithm is order dependent.
The paper said: "When the PC algorithm is applied to data, it is ...
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Do I need to use nested cross validation to select the best algorithm?
Suppose I want to compare the performance of several classification algorithms on a data set to choose the best algorithm to use with that data set. To do this, I run logistic regression, k-nearest ...
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Least likely sample in Multivariate Hypergeometric distributions
Let $U=(U_1,U_2,\dots,U_c)$ being an urn with $U_i$ balls of color $i \in [1,c]$ and $\Omega(U)$ be the set of possible draws from urn $U$. For $D \in \Omega(U)$ the probability of drawing $D$ is ...
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Why does my regression neural network completely fail to predict some points?
I would like to train a NN in order to approximate an unknown function $y = f(x_1,x_2)$. I have a lot of measurements $y = [y_1,\dots,y_K]$ (with K that could be in the range of 10-100 thousands) ...
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references of concept of scipy.signal.argelextrema
I am looking into the concept of algorithm code scipy.signal.argelextrema I understand the concept, but, I am trying to find an article\review\book chapter which ...
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Capital Y vs small y
I want to use the acceptance-rejection method to generate a sample from the following target probability density function:
$$f\left(x\right)=1.25x^4+2x^3+0.25,\:0<x<1$$
Let the trial probability ...
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Why do gradient boosting algorithms mostly use trees?
Why do gradient boosting algorithms mostly use trees? Is there any logic in this? (in XGBoost and in boosting which in sklearn library uses trees, not other algorithms).
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Identify Stock Outages Using Only Sales Data
I have a dataset of sales data for a retailer for a number of SKUs. I'm trying to use this data to identify when a particular SKU was out of stock. I do not have purchase order data (the retailers ...
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Statistical/Automated method for identifying a dataset suitable for Machine Learning Modelling
Given a folder which has 10k Excel files, the objective is to identify the datasets suitable for Machine Learning Modelling Approach.
We use a script right now which performs this operation and call ...
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What are the best ranking models availble?
I am looking to compare data for example with sports teams, compare recent and historical results. I want to be able to compare any two teams when needed and also take into account a players database ...
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Comparing Two Ensemble Methods
Algorithm1 uses a single base classifier as a member of the ensemble. Suppose the size is 5 and each member in the ensemble is a Naive Bayes. The training data is shuffled/sampled (may generate a ...
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Examples in the Real World where Evolutionary Algorithms/Genetic Algorithms Outperform other Classes of Optimization Algorithms
I have been trying to do some research to find out if there are certain industries/types of problems or even specific examples in applied research paper where Evolutionary Algorithms (e.g. Genetic ...
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Estimating a statistic by combining two different data sources
Say you want to estimate a statistic $\theta$ and have two data sources. A sample from data source A can be treated as a low-variance, somewhat biased estimate of $\theta$. A sample from data source B ...
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What are the best algorithms to predict a continuous target where I only have binary attributes? [closed]
Hello, I am new to machine learning and have a project where the dataset consists of binary attributes and the target("Pawpularity") has a continuous value. I was wondering if you could ...
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How can I reduce the number of times people are randomly assigned to the same team when using a random number generator?
I am randomly assigning people to play on different golf teams each week. I have 7 teams, each with 4 players.
My starting list is ordered alphabetically by last name.
I’m using a random number ...
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Vapnik's supposedly bounded functions
In his Statistical Learning Theory (1998), Vapnik presents the following mixture of two normal laws (p.236), in which the parameters $a$ and $\sigma$ are unknown:
$$p(z;a,\sigma)=\frac{1}{2}N(a,\sigma)...
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What types of algorithms can deal with data spikes by distributing them into the prior values, rather than removing them?
I'm dealing with a dataset that has three types of inputs:
Normal daily measurements
Periodic 'data dumps' that contain additional values for the past 30 days
Periodic 'data corrections' that might ...
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How would one measure consistent high values for a dataset? Is it the same as measuring a high variance for a low percentile?
For a numerical dataset, you can measure the consistency of the data using variance, where lower variance means the data is closer to the mean/median, while higher variance means the data is spread ...
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Algorithm design in data science [closed]
I am trying to enter into a data science job. I gave some internship interviews but have not had luck yet. Last week, I gave an interview where one of the panellists asked one hypothetical question:
“...
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Data association on data from multiple cameras
Suppose we have several cameras that cover a certain area. In each camera we track a person. Each person have a path in global coordinates, timestamps and a feature-vector. The goal is to group these ...
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Without looking at all the data, how can I test whether a collection only contains unique objects?
I have collection of data objects distributed across multiple machines. An O(n) lookup is not feasible, so I will need to sample. Is there an algorithm that I can use (preferably one that can relate ...
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Algorithm/method for grouping items based on their relative distance
I'm looking for a method to classify a set of items based on their relative distance.
For example assume we have 4 cities and we know their relative distance:
city1
city2
city3
city4
0
2.1
2.2
3.4
...
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Different way to do PCA: overall comparison
Given a dataset PCA can be performed via 3 ways:
Eigenvalue decomposition
Singular value decomposition
Non-linear iterative partial least-squares algorithm
Can anyone shed light on comparative study ...
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RL algorithms for continuing task problems
I am stuck with an RL problem where the state space is continuous and the action space is discrete. The problem is a continuing task problem; there are no episode boundaries.
So far, I have tried to ...
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Are there any General Proofs on Genetic Algorithms?
Are there any general proofs or theorems relating to "genetic algorithms"?
I have been reading about a theorem in math called the "Schema Theorem" - this theorem is one of the ...
- 5,892
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Rule-Based and Tree-Based Statistical Models
I have been doing some research and have been trying to find "Rule-Based" and "Tree-Based" (statistical) models that are capable of overcoming the "greedy search algorithm&...
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Is there a concept for "almost parallel" edge?
By "almost parallel" I mean if set S of closing nodes has many conflicting edges with set T of closing nodes, then the edges between them are almost parallel. The emphasized edges in this ...
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