Questions tagged [probability]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
2
votes
1answer
28 views

Predict_proba() probabilities distribution [closed]

I’m trying to calculate probability of class 1. I’m using gradients boosting (catboost classifier) Is it normal to have an equal rate of positive classes in every predict_proba() bucket? e.g.: [...
0
votes
0answers
10 views

Is using cross-entropy enough to ensure the output is a distribution probability?

I am following along https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html. In this code, the last layers of the pretrained networks are linear. The loss used in this ...
1
vote
1answer
14 views

Adjust predicted probability after smote

i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model. I have two ...
0
votes
0answers
22 views

Statistical loss function for categorical distributions

For training an autoencoder model whose outputs (and inputs) are parameters from a categorical distribution $[q_1, q_2, \ldots, q_n]$, I have to define a proper loss function measuring the distance ...
0
votes
0answers
22 views

Why Maximum Likelihood Estimation for normal distribution?

Since we can compute the mean and the standard deviation of a set of random variables, why do we use Maximum Likelihood Estimation to estimate these parameters?
1
vote
0answers
8 views

Hidden markov model to detect how loaded a die is?

I was wondering if you could make a hidden markov model to decide what the optimal loaded ness of a die is. I know normally you would want to do something like take a prior like I know of two types of ...
0
votes
0answers
18 views

Learning discrete probability distribution that is parametrized by a set of real-valued parameters

Assume I have a discrete probability distribution defined over binary variables. This probability distribution is parametrized by a set of real-valued parameters, which all are contained in a segment, ...
0
votes
2answers
49 views

What is the meaning of “probability distribution of p(x)” of something uncountable?

I'm studying VAE and new to both of the neural network and the statistic. After some researches, I could understand the rough concept of VAE. But what makes me confused is, the meaning of probability ...
0
votes
0answers
10 views

Generating correlated time series with different probability distributions

I have the following problem to solve: I need to generate two time series, each modeling a variable (lets call them VarA and VarB). For each of these variables, I have a PDF (PdfA, PdfB) which belongs ...
0
votes
0answers
50 views

Tensorflow Probability Example : Gradient Computation Error

I am trying to run example code from tensorflow Probability for Dirichlet Process Mixture Model (https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/...
0
votes
1answer
22 views

Probabilities of a Poisson distribution not making sense

I am trying to find probabilities of orders a restaurant gets on Sunday's. For last 6 months average orders are 1000 without any big anomalies like 700 or 1300. This is a case of poisson distribution &...
0
votes
0answers
9 views

How to estimate claims for warranty analytics where continuous improvement reduces warranty claims?

I have a situation where a company who has warranty claims on its product and they want to know that is it possible for them to extend that. Now the case is that they do improvement on the existing ...
0
votes
0answers
11 views

Prediction interval and probability to increase

Let be $y_i$ some observed values of a given time-series. We denote $\hat{y}_i$ the corresponding predicted values. We also assume to have a prediction interval $p_i$ such that $\hat{y}_i \in p_i = [\...
0
votes
0answers
5 views

Estimating “child” level probability based on historical proportion?

I have a classification model that predicts whether a product (eg chair) will be a champion. A champion product is defined as products exceeding a certain monthly sales threshold. However, if i want ...
1
vote
1answer
54 views

Should scaling be done for mixed data (categorical and numerical)?

My dataset contains 13 attributes consisting of 10 Numerical and 3 Categorical attributes and Target. It has 180 observations ...
1
vote
0answers
34 views

how to use Bayesian theorem and probabilistic analysis? [closed]

could someone help me to solve this problem please ? Your prize Rapid Ripe tomato plant has flowered and is ready to start producing fruit. If all goes well, ...
0
votes
1answer
34 views

What do we learn from training a dataset for logistic regression

What do we learn from training our dataset in Logistic Resgression? Like in Linear Regression, with the help of training set we are able to generate a best fit line(y = mx+c) where m and c come from ...
2
votes
1answer
34 views

How to find the probabilities of certain events occurring in a defined sequence?

Good day, everyone! I have a problem where I have to program something like this: I have some arbitrary number of events, let's call then Event A, B, C, D, E, F, ... and so on. Now they occur in ...
3
votes
1answer
57 views

Relation between an underlying function and the underlying probability distribition function of data

I heard and read a lot of times the following statements and got a lot of confusion over time. Statement 1: The goal of machine learning is to get a function from the given data Statement 2: ...
0
votes
2answers
45 views

Solving Bayes Theorem equation -> I can't calculate proper result

I am solving questions for an edx course on Machine Learning. One particular question is giving me a problem: Assume a patient comes into the doctor’s office to test whether they have a particular ...
1
vote
1answer
21 views

What is meaning of different symbols in image?

I am learning about Deep Generative Models, tutorials all over the places use symbols and no one actually explaining the meaning of it. Can you please suggest the meaning of these terms?
0
votes
0answers
13 views

What is to be done when PDFs are not Gaussian/Normal in Naive Bayes Classifier

While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, ...
1
vote
1answer
755 views

Pytorch doing a cross entropy loss when the predictions already have probabilities

So, normally categorical cross-entropy could be applied using a cross-entropy loss function in PyTorch or by combing a logsoftmax with the negative log likelyhood function such as follows: ...
0
votes
0answers
10 views

Which distribution should I use for Naive Bayes algorithm(Gaussian or Rayleigh)? What to do with categorical data?

I am predicting whether credit card application of an individual would be approved or not given his/her credentials. I have the following dataset: The variable descriptions are as follows: I need ...
0
votes
0answers
21 views

Issue with using PCA on MLPClassifier

I'm trying to tune my MLPClassifier using GridSearchCV, but it takes ages, so I was wondering if using PCA data will decrease ...
0
votes
0answers
40 views

what is the posterior distribution of this problem

what is the posterior distribution given that our model has the following prior $x_1,x_2,x_3.......x_n~N(μ,σ_0^2)$ and $μ~N(m_0,s_0)$ with $m_0,s_0$ and $σ_0^2$ being known ? I cannot figure is it ...
0
votes
0answers
43 views

How can we compute likelihood in recurrent neural networks?

Suppose that we have a recurrent neural network (RNN) with length $T$ for a classification task that generates an output at every time step which is a probability distribution over classes obtained by ...
0
votes
0answers
9 views

Neural Net to represent Multivariate Distribution

I want to use a neural net to learn an arbitrary multivariate distribution (say, 3 variables). If this was in one variable, I would make the neural net monotonically increasing to have it represent ...
0
votes
1answer
14 views

Classification targets with heterogenous meanings

I am training a classification model on a dataset of users on a website and each has 100 different measurements of their behaviour on the platform. Most of these ...
0
votes
1answer
21 views

Sample Space of Longest Run

I am trying to find a way to calculate all possible combinations of a sequence that have a certain length of long run. When answering questions regarding sequences of heads and tails, sometimes ...
0
votes
0answers
9 views

Get sequential model to output probabilities with pystruct

I have implemented a sequential model using Pystruct. The model I use is BinaryCLF and as a learner the StructuredPerceptron. So far, when I test the prediction of my model, I give as an input the ...
2
votes
1answer
118 views

Data-generating probability distribution, probability distribution of a dataset, in ML

In Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016 Nov 10. http://thuvien.thanglong.edu.vn:8081/dspace/bitstream/DHTL_123456789/4227/1/10.4-1.pdf p. 102 (for example), it is said ...
0
votes
0answers
14 views

How to interpret the transition and feature weights in a Conditional Random Field model?

Conditional Random Fields model have been a popular method for Named Entity Recognition as it accounts for statistical dependencies between entities and can include observed features that can aid with ...
1
vote
0answers
19 views

F-test for comparing the mean of two groups

What would be the procedure, in terms of F-test, if I would like to check if the mean of one group is greater than the other group (alternative hypothesis: $\mu_1 > \mu_2$)? And also what would ...
0
votes
0answers
18 views

Build model to get daily probability of meeting a certain end of period goal

I was hoping for some consultation and direction with how to go about the following: To give context, I work for an agency that manages advertisements on social media for general motors - ...
3
votes
2answers
155 views

Seeking advice on knowledge discovery

Background Information I work for a fire department in Florida and the fire chief posed a question to me; At any given moment in time during the calendar year 2018, how many fire trucks are busy, ...
0
votes
1answer
631 views

How do I properly write scipy.stats.binom.cdf() details

I need to calculate the probability of my random variable being $\le 0$. It's a binomial distribution, $10000$ trials, probability of success is $\frac{10}{19}$ (roughly $0.53$). How do I properly ...
0
votes
0answers
16 views

What's the correct/preferred way of determining the final class from seq2seq softmax probabilities?

Here's an example, between three classes a, b and c and their softmax probabilities from an imagined seq2seq algorithm. In this case it's pretty obvious that class c is the most likely single label ...
1
vote
1answer
43 views

How can I make ROC and compute AUC?

I created a boosting tree and got the probability for each tuple in my testing set. But I'm confused on how to combine each probability. Can someone tell me how to combine the probabilities?
0
votes
0answers
18 views

How should I visualize image choice rankings and probabilities?

I need some help visualizing some data from a choice experiment. There were two sets of pictures that folks get to see. The first set contains 6 pictures. Participants pick their top two (based on ...
0
votes
0answers
12 views

create joint prob distribution or empirical relation for two variables

There are two variables, X1 and X2. The experimental study shows that they are highly correlated. Are there any reliable ways to create an empirical mapping(or equation) between X1 and X2. Assuming ...
0
votes
0answers
7 views

Confidence vs. Count in association rule mining: which one is better?

I am writing a program that mines association rules from a large data set. I have an array of association rules, and I have to decide which ones are more representative of the patterns I am studying. ...
0
votes
0answers
20 views

How is the standard deviation of VAE's obtained?

I am trying to build a Variational Autoencoder. I was looking at various codes online and found most of them in some way or another copy Francois Chollet (Google researchers) code. Now my main ...
1
vote
1answer
39 views

Predict how many days late or early someone will finish their work

So I have a set of deadlines and people, with a database of when those people finished their previous work and how much after the deadline it was, as well as when the work was given. The work itself ...
0
votes
1answer
51 views

Very low probability in naive Bayes classifier 1

I have some training data (TRAIN) and some test data (TEST). Each row of each table contains an observed class (X) and some columns of binary (Y). I'm using a Python script that is intended to predict ...
1
vote
0answers
20 views

Probabilistic guarantees for SQL query execution

We are working on a project that tries to accelerate Spark SQL using dedicated hardware. Our approach works much better (in terms of the resulting latency) if we allow query processing that is only ...
1
vote
0answers
47 views

Various Distances between probability distributions

I was reading about different distribution distance - and came across Kullback-Leibler divergence, Jensen-Shannon divergence, MMD, and Wasserstein distance - the book was too abstract for me to absorb....
1
vote
1answer
50 views

Probabilistic Machine Learning model to match spatial data

I have spatial data from multiple sources. This data consists of ID, lat, long, and time. My goal is that given a new lat-long, the model needs to return (preferably with a probability) the data ...
1
vote
2answers
211 views

Why does a belief network need to be represented using a directed acyclic graph (DAG)?

I would have thought that it's because DAGs preserve the dependency relationships between the variables, but I am currently unsure. Thanks
2
votes
2answers
319 views

Why do people use CrossEntropyLoss and not just a softmax probability as the loss?

I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1....