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Questions tagged [probability]

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12 views

Aggregate function for Machine Learning scores

I am trying to find a way to calculate an aggregate score between 0-100 for multiple scores coming from a machine learning logistic regression algorithm. For example, if I have 3 scores: 20, 50 and 60 ...
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9 views

Do I need to correct predict_proba by training fraction?

Many algorithms provide a predict_proba function indicating probability of a case to belong to that class (e.g. https://scikit-learn.org/stable/modules/generated/...
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7 views

What are some limitations of using Collaborative Deep learning for Recommender systems?

Recently I worked on a paper by Hao Wang- Collaborative Deep learning for Recommender Systems which uses a two way tightly coupled method, Collaborative filtering for Item correlation and Stacked ...
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9 views

Why GP (Gaussian Process) works?

Actually I read through some papers about GP(google the stanford university and oxford university lecture pdf and read) I knew the procedure to do it with detail to choose the kernel and test and the ...
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1answer
17 views

How to predict unknown(hidden) value by incomplete value or partly recorded value

Let me make it clear by make an example: Suppose I knew a person's cost each month for 3 years like: 2016Jan : $2500 2016Feb : $4000 2016Mar : $3500 ... Just according to this, can I ...
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26 views

Comparing two sets of exponential data T(t)=−e^(−kt)

I have two sets of exponential data (temperature measurements) of the form: $T(t)=−e^{(−kt)}$. $k$ is a constant that determines the rate of temperature change. 1 temperature measurement was taken ...
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13 views

Probability of word in Latent Dirichlet Allocation Given Document and Topic

I need to find the prob of word given topics - K and docs - D, if I have $\theta$ and $\beta$ for example P($w_4$| z=2,Docs = 3) = ? (Probability of the fourth number on dictionary in topics 2 and ...
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1answer
23 views

Extending DTW 1-NN Classification to On-line Scenario

I am familiar with Dynamic Time Warping classification using a 1-nearest neighbour approach. However, in most benchmark datasets and applications, it used ex-post, i.e. classifying a time series after ...
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1answer
24 views

objective in policy gradient equation?

I don't understand how this was deduced from first equation to second expectation. Is it from conditional probability theory? I checked but still can't understand. From wikipedia, the expectation of a ...
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0answers
12 views

What is the correct way to calculate the entropy of a language model on a data-set of sentences?

I want to fit the parameters of my language model by minimizing the entropy/ maximizing likelihood of my language model on my data-set. However, I am uncertain as how I should go about doing this. ...
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0answers
7 views

Does bayesian model uncertainty of prediction depends in any way on prediction input?

In bayesian deep learning we express uncertainty as P(w|x,y). Both x and y represent our training dataset. Does this mean that for every test sample the uncertainty of model output doesn't depend in ...
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2answers
38 views

Maximum likelihood estimation vs calculating distribution parameters “manually”

I'm sorry for asking probably elementary question, but I cannot understand how estimating probability distribution parameters using maximum likelihood estimation method differs from calculating these ...
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1answer
21 views

Statistical machine translation word alignment for FR-ENG and ENG-FR: what is p(e) and p(f)?

I'm currently trying to implement this paper, but am struggling to understand some of the math here. I'm pretty sure I understand how to implement the E-step, but for the M-step, I'm confused on how ...
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1answer
68 views

Capturing movement importance - logistic regression output

I'm studying some event for a set of objects that can be plotted on a square $[0, 100] ^ 2$. I have used logistic regression to calculate probabilities that event occur for different objects and the ...
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1answer
41 views

Can someone please explain what this sample function is upto?

So there is a function in Dino_Name_Generator at Deeplearning.ai notebook ...
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3answers
385 views

Why does the naive bayes algorithm make the naive assumption that features are independent to each other?

Naive Bayes is called naive because it makes the naive assumption that features have zero correlation with each other. They are independent of each other. Why does ...
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0answers
11 views

Derivation Of H Entropy Function

https://tdhopper.com/blog/entropy-of-a-discrete-probability-distribution/ How can I derive the $H$ function based on the three assumptions below? And how is $H$ related to $log$?
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1answer
31 views

mathematical accurate definition of the binary independence model

I have a hard time understanding the exact mathematical meaning behind the binary independence model. On wikipedia we can see the following definition or similarly in the book from Manning and ...
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1answer
46 views

Help solving Bigram Model with the following probabilities

I came across the following problem involving bigram models which I am struggling to solve. Following this tutorial I have a basic understanding of how bigram possibilities are calculated. Problem: ...
2
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1answer
78 views

Non-mutually exclusive classification sum of probabilities

So I have the following problem: I realized (while writing my master thesis) that I am still not sure/have vague descriptions of some of the machine learning principles. I already asked one question ...
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3answers
292 views

How to convert an array of numbers into probability values?

I would like some help with respect to certain numerical computation. I have certain arrays which look like: Array 1: [0.81893085, 0.54768653, 0.14973508] Array 2: [0.48078357, 0.92219683, 1.02359911]...
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0answers
17 views

Time Series Autocorrelation Estimation

How do I find joint PDF f(xi,xj) needed for estimating autocorrelation from 1-D time series data X, using the kernel windowing technique? I am well aware of kernel windowing technique for estimation ...
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0answers
7 views

Which features of a data set can be used for market campaigning using propensity scores?

A dataset contains so many fields in which there is both relevant and irrelevant field. If we want to do a market campaigning using propensity scoring, which fields of the data set are relevant? How ...
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1answer
75 views

Poker tournament winner prediction

I am trying to solve poker tournament winner prediction problem. I’ve millions of historical records in this format: Players ==> Winner P1,P2,P4,P8 ==> P2 P4,P7,P6 ==> P4 P6,P3,P2,P1 ==> P1 I ...
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1answer
124 views

PMI between lemma vs surface

I was wondering whether it's possible to compute the some sort of pointwise mutual information between lemma and its surface form. First if we assume, ...
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1answer
24 views

Differentiating roadmap of a loss function

Let's say I'm performing Stochastic Gradient Descent (SGD) on binary cross entropy error while optimizing weight $w_{2}$. Binary cross entropy error: $$L(y|p(x_{i}))=-y_{i}*ln(p(x_{i}))-(1-y_{i})*...
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1answer
46 views

How to generate a sample from a generative model like a Restricted Boltzmann Machine?

I am learning about the Boltzmann machine. So far, I have successfully written a code that can learn the coefficients of the energy function of a Restricted Boltzmann Machine. Now, since my model is ...
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0answers
6 views

Measuring minority class frequency (with confidence interval) with calibration data

I have a large amount of unlabeled data, a binary classifier that outputs a score (uncalibrated) and a small amount of labeled data that was used to create a calibration plot. When I run my ...
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0answers
536 views

How does binary cross entropy work?

Let's say I'm trying to classify some data with logistic regression. Before passing the summed data to the logistic function (normalized in range $[0,1]$), weights must be optimized for desirable ...
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0answers
23 views

correcting conditional and marginal distribution in transfer learning

I understand that in case of transfer learning, we can have the target and the source data having different domain distributions. In such cases, authors in many papers suggest bringing the marginal ...
1
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1answer
20 views

Predicting object by features probabilities

I have the definition of an object provided as features probability. Each object has it's own feature importance and probabilities. For example for object "X", I have "color" feature (with the weight ...
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3answers
66 views

Distance between very large discrete probability distributions

I have 192 countries where each country has some value for 1 million attributes which sum up to 1 (a discrete probability distribution). For any one country most of the values for the attributes are 0....
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0answers
23 views

Apparent Paradox between Principle of Maximum Entropy and Perplexity

As I found the principle of maximum entropy said: the probability distribution which best represents the current state of knowledge is the one with the largest entropy And the definition of ...
2
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1answer
50 views

How can I estimate user-item purchase probabilities of a e-commerce website?

I am writing my Master thesis, where the goal is to estimate user-item purchase probabilities. In other words, for a given user, what is the probability he/she will buy a certain item. I have session ...
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0answers
10 views

How do we create the graph structure required for unimodal bandits

Some of the papers describing Unimodal bandits are [1], [2] and [3]. The common factor in all these algorithms is that they require a graph as input whose nodes are arms (actions) and an edge exists ...
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0answers
330 views

keras categorical CNN prediction always output [[ 0. 0. 0. 0. 0. 0. 1. 0.]] No probability for other classes?

print(model.predict(image)) print(model.predict_proba(image)) print(model.predict_classes(image)) Using the above different predict methods I always get ...
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1answer
49 views

Which machine learning model should I learn for this problem?

I'm working in python. Would like to practice some machine learning, and I've always been curious about an analog to the problem below... A collection of 3 letters are drawn randomly from the 26 ...
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0answers
17 views

Optimality of Bayes classifier

I've studied an introdution to the bayer optimal classifier and i've understood how it works. What isnt clear to me is why it's considered optimal, and what it means to be optimal in this case. Can ...
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0answers
20 views

Machine learning with stochastic labels

I'm sure this is a standard problem in machine learning so links to pages or books that discuss the problem are also welcome. The problem is: We have some input data points, $x_i$, and some output ...
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0answers
38 views

Generative Model of Bayesian Statistics

My question is regarding generative models to solve for probability once our outcome is known (but our probability of having achieved that outcome is not). https://www.youtube.com/watch?v=3OJEae7Qb_o ...
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0answers
56 views

Using Random Forest Probabilities for customer sensitivity

I am trying to build out a customer sensitivity model to price increase. My hypothesis is that as "Annual Fee" to a product subscription increases so will the probability of a customer to churn. I ...
3
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1answer
27 views

Can the 'bin size' in a histogram be thought of as a regularity constraint?

When thinking about a histogram as an estimate of the density function, is it reasonable to think of the bin size as a parameter that constrains the local structure of that function? Also, is there a ...
0
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1answer
63 views

How do I convert an L2 norm to a probability?

I am using the dot product as a way to measure the similarly of two facial-model vectors extracted by a ML algorithm (OpenFace in fact). I would like to convert the L2 norm to a probability U[0,1] in ...
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1answer
142 views

Is recall more important than precision for mass mailings?

Say for example, I built a classification model for a mailing campaign that will be applied to 1M records. The positive class for the model would be customers and the negative records would be non-...
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0answers
188 views

Calculating an estimate of KL Divergence using the samples drawn from distributions

Given two sets of samples drawn from two different distributions, is it computationally possible to get an estimate of KL-Divergence between the two distribution using these samples? Here I am ...
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0answers
29 views

Can we calculate propensity score individually?

I have temporal twitter data, and I want to calculate propensity score for the treatment and control group. The problem is, the treatment happened at different time for different user, and I want to ...
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0answers
10 views

How to compute joint distribution of a tree using belief propagation?

Suppose now I have a deep Boltzmann machine with a full binary tree structure. Only the leaf nodes are visible variables. All other nodes are hidden variables. Belief propagation(sum product )can be ...
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0answers
51 views

Where does the name “Variational” Inference come from?

While I understand how this method is used to approximate posteriors, I don't quite get why it should be called Variational Inference can go through this link for reference. I have limited knowledge ...
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0answers
54 views

Predict class having only class proportions for every attribute (non labeled data)

I am working with a big data set (millions of observations) where for each observation I am trying to predict a probability (or score it) of being of a class. I haven't any labeled training data and ...
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0answers
34 views

record linkage for order tracking

I have a use case that I need to find how to resolve it : This use case is about tracking purchase order from the customer until the industry. ...