Questions tagged [probability]

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Regressor to estimate probability from samples

What is a good way to estimate probabilities in a dataset of samples? Each sample is a measurement, that is usually 1 or 0. The goal is to calculate probabilies based on all feature rows. Simple ...
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7 views

How to calculate Probability from MSE results?

I am using a MSE classifier to classify some binary images into 4 classes, I extract 8 dimensional features for classification and normalize them to the max feature of them, so the max value for any ...
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0answers
26 views

How to find out the likelihood of some distributions?

I have a data with 40 class. Every class consist 2 features with normal distribution. I assume that some of the class has no significance differences, and I'd like to decrease the number of class by ...
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1answer
18 views

Clarifying Probability Mass Function (PMF)

I am currently reading Deep Learning book, and I want to get better understanding of probability theory. In chapter 3.3.1 of Deep Learning book it states that: Often we associate each random ...
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1answer
14 views

How to perform link prediction in text based relationship data

I need to establish if there is a link between 2 columns from two different datasets with one matching column, where; ...
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1answer
16 views

Convolution and Pooling as Infinitely strong priors

I am currently reading about Convolutional Neural Networks in Deep Learning Book. I am stuck on section 9.4 titled "Convolution and Pooling as Infinitely String Priors". Could someone please ...
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1answer
81 views

How to compute denominator in Naive Bayes?

Suppose we have class C_k and input feature vector x in dataset How to calculate probability ...
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13 views

Practical example and working of Laplace Smoothing or Linear Interpolation in Natural Language Processing (NLP)

Let us suppose we have a document where total_words = 50 (for example -> is,the,now,is,am,here,now) total_unique_words = 40 (for example -> is,the,am,here,now) How can we apply the ...
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1answer
16 views

Understanding these probabilities, and reading the pdf and cdf

UPDATE: this is a math question but as i would like to codify it, I thought the DS SE might be appropriate. I am trying to sketch out this problem statement. Where am I going wrong since I don't see ...
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0answers
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overlapping of surfaces with holes

I have two disks, of equal diameter. Both disks have a 'spatter' of holes, in random positions (from a two-dimensional - i.e., surface - uniform distribution; in other words, holes close to the sides ...
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1answer
22 views

Control which features are used for every task in multioutput classification?

I would like to perform a multiclass-multioutput classification task, on vectorized textual data. I started by using a random forest classifier in a multioutput startegy: ...
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21 views

Bias of an estimator?

What is p(X;0)actually modelling? What's the difference between the two types of theta? What's happening in this equation and did expectation convert into summation ? Source:https://subscription....
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1answer
57 views

why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?

Im trying to understand why naive is needed in Naive Bayes and everyone says Naive Bayes assumes the input features (predictors) are not correlated hence they are not dependent on each other . i want ...
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Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
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1answer
29 views

What is the name of this statistical interaction?

What is the name of the following statistical / informational interaction: given A, I know exactly what B is. given B, I know to some extent what A is. I'm not looking for a probability but rather ...
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1answer
42 views

XGBoost: How to set the probability threshold for multi class classification

I am using the XGBoost for classification of text data. There are the 3 different classes in the training dataset. ...
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0answers
9 views

Is it possible to fit GMM so that mean lies outside of image bounding box?

I m currently reading https://arxiv.org/pdf/1411.3159.pdf They take gradient maps and fit Gaussian Mixture Model. Then they evaluate how often is the activation center inside of the image bounding box....
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1answer
25 views

How to interpret axis of Histogram and distribution curve?

After doing statistical analysis of data I got the below figures: The data contains a line length in meters which is represented with Line(m). The purpose here is to confirm the distribution behavior ...
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0answers
11 views

Identify franchise names in a list of names

I am experimenting with a string vector of 200.000 observations to create a feature that determines if the name belongs to a franchise (Burger King, Mc'Donalds, etc). The present strategy is to ...
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0answers
26 views

Gaussian mixture models: find correct prior

This task might be pretty simple to the most of you. However, I am struggling to calculate the PDF for the Gaussian distributions. Help is very much appreciated!
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How does one use activation function with greater than [-1;1] range for binary classification?

In Efficient Backprop (http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf), Lecun and others propose to use activation function that don't reach target values on their asypmptotes. They explain (§ 4....
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1answer
19 views

Rules/Methods to use for determining user groups with no review data

I’m working on an analytical problem where I have customers who deal with end consumers. End consumers can leave customers reviews based on their experience (Star ratings, text etc). I’m trying to ...
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0answers
18 views

Calculate marginal probability distributions of a dataset

I have a dataset with 'n' features and a label(binary) corresponding each entry. I am using a predicitive model to predict these labels using those 'n' features. Now, I wish to know the marginal ...
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1answer
55 views

Confidence value for face recognition

In the context of face recognition I have the following histogram: blue bins count the comparison distances for "self matches" (comparing two images of the same person). Orange bins count the ...
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1answer
27 views

Synthetic time series generation according to some distribution

I'm trying to develop a change detection model that uses sliding windows. Given a time series with some features I've a sliding widows that analyses that time period and compares with a successive ...
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0answers
22 views

Difference between Gibbs sampling and variational Bayes inference

After reading in blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. Below link described but it's difficult to ...
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1answer
90 views

Re-sampling of a Histograms Bins

I would like to be able to resample a histograms bins without having access tot he raw data. And just to be clear, by resample, I mean to change the number of bins and still provide a good estimate ...
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3answers
194 views

What is the meaning of likelihood?

I am studying Bayes probability applied to machine learning, and I have encoutered the concept of likelihood, which I don't understand. I have seen that the Bayes rule is: $P(A|B)=\frac{P(B|A)P(A)}{...
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1answer
280 views

Find the top n features from feature set using absolute values of `feature_log_prob_ ` parameter of `MultinomialNB`

I am working on Donors choose dataset and have converted categorical, numerical and text features into vectors. I want to find the top 20 features from my 5095 features using absolute values of ...
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1answer
63 views

Implementation explanation for predict_proba in RandomForestClassifier- sklearn

Attached is an extract from the RandomForestClassifier documentation of sklearn. The last line,"The class probabilities of a single tree is the fraction of samples of the same class in a leaf." is ...
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12 views

What is the likelihood of this function

I have been given that the outputs from a feed-forward network ( one output ) follows$ Y |X = x, w ∼ N(\hat{y} (x, w), σ^2)$ * From what i know, the likelihood function is defined as $p_{X|\theta}...
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1answer
75 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.: [...
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15 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 ...
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1answer
53 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 ...
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0answers
83 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 ...
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0answers
29 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?
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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 ...
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0answers
19 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, ...
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2answers
53 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 ...
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0answers
12 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 ...
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0answers
133 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/...
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1answer
25 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 &...
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0answers
10 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 ...
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0answers
12 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 = [\...
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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 ...
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1answer
120 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 ...
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0answers
44 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, ...
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1answer
37 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 ...
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1answer
42 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 ...
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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: ...

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