Questions tagged [probability]
Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur or how likely it is that a proposition is true.
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XGBoost outputs tend towards the extremes
I am currently using XGBoost for risk prediction, it seems to be doing a good job in the binary classification department but the probability outputs are way off, i.e., changing the value of a feature ...
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Are the raw probabilities obtained from XGBoost, representative of the true underlying probabilties?
1) Is it feasible to use the raw probabilities obtained from XGBoost, e.g. probabilities obtained within the range of 0.4-0.5, as a true representation of approximately 40%-50% chance of an event ...
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Loss Function for Probability Regression
I am trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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Confidence intervals for binary classification probabilities
When evaluating a trained binary classification model we often evaluate the misclassification rates, precision-recall, and AUC.
However, one useful feature of classification algorithms are the ...
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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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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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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 ...
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Xgboost predict probabilities
When using the python / sklearn API of xgboost are the probabilities obtained via the predict_proba method "real probabilities" or do I have to use ...
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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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How useful is Bayesian Inference
Last few months, I had been exposed to Bayesian Inference in ML course
With further investigation, I come to place where there is MCMC technique to simulate the ...
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How to get probabilities values with keras?
tensorflow version = '1.12.0'
keras version = '2.1.6-tf'
I'm using keras with tensorflow backend.
I want to get the probabilities values of the prediction.
I want the probabilities to sum up to 1.
...
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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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Compute parameters of a PDF (probability density function) for which no closed form expression is available
I would like to compute parameters such as mean, variance, quantiles, etc. for a PDF which is only given as a piece of code. That is, it can only be evaluated numerically at given points; no closed-...
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Very low probability in naive Bayes classifier
I have designed NB classifier from scratch in python for binary classification problem. There are total 220 records out of which 85 records belongs to 'Yes' class and 135 to 'No' class. My classifier ...
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What does a predicted probability really mean, without considering the accuracy of the underlying model?
Say I've built a (completely unrealistic) classification model in Keras that gives me 1.00 accuracy.
And next, I would like to use my model on some new, unseen data, and use ...
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why does my calibration curve for platts and isotonic have less points than my uncalibrated model?
i train a model using grid search then i use the best parameters from this to define my chosen model.
...
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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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Using softmax for multilabel classification (as per Facebook paper)
I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...
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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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Analysis of probability distribution of each features and Machine Learning
While I know that probability distributions are for hypothesis testing, confidence level constructions, etc. They definitely have many roles in statistical analysis.
However, it is not obvious to me ...
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Notation for features (general notation for continuous and discrete random variables)
I'm looking for the right notation for features from different types.
Let us say that my samples as $m$ features that can be modeled with $X_1,...,X_m$. The features Don't share the same distribution (...
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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: The goal ...
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xgboost or lightgbm to handle Binomial problems [duplicate]
I have a dataset containing a column of trials, a column of successes and other features; and, obviously, I can generate a probability column. I would like to use gradient boosting methods (like ...
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What is difference between Bayesian Networks and Belief Networks?
While reading some articles about Bayesian Networks, I came across many occurrences of Belief Networks.
Do both of these terms mean the same thing or is there any difference between Bayesian Networks ...
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How to use different classes of words in CountVectorizer()
Suppose I have a piece of writing and I want to assign probabilities to different genres (classes) based on its contents. For example
Text #1 : Comedy 10%, Horror 50%, Romance 1%
Text #2 : ...
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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:
...
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Softmax classifier never allows for 100% probability in LSTM?
When working with LSTM I am using a softmax classifier and a one-hot encoded vector approach. The softmax looks like this:
$$S(h_i) = \frac{e^{h_i}}{\sum e^{h_{total}}}$$
notice, LSTM's result is a $...
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why we sample when predicting with Recurent Neural Network
I trained a Recurrent Neural Network to predict the next word in a sentence.
I trained and now I want to predict, but there is something I am not getting well.
I saw it in many tutorials even in the ...
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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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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, ...
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Why does a belief network need to be represented using a directed acyclic graph (DAG)?
I would have thought that it was because DAGs preserve the dependency relationships between the variables, but I am currently unsure.
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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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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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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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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 ...
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Selecting the right algorithm for match probability prediction
Looking for assistance kick-starting a new machine learning scenario. In this case I need to pair one entity (ex. person) with a group of entities (ex. other people) given a history of matching ...
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which loss function (if any) optimizes the calibration graph
The calibration graph is the predicted versus actual probability(see http://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html). Is it possible to optimize the ...
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What is the difference between KL-divergence, JS-divergence, Wasserstein distance and MMD?
I was reading about different distribution distances, and came across
Kullback-Leibler divergence
Jensen-Shannon divergence
Wasserstein distance
Maximum mean discrepancy (MMD)
The book was too ...
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How to make machine learning model that reports ambiguity of the input?
Suppose I want to build a neural network regression model that takes one input and return one output.
Here's the training data:
...
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Marginalization of joint distribution
I am trying to understand how you marginalise a joint distribution.
In my case I have a fair coin, $P(C) = \frac12$ and a fair dice $P(D) = \frac16$. I am told I win a prize if I flip the coin and ...
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How to predict the probability of an event?
I have a dataset where a set of people donated for charity along with the dates of the donation. I have to find the probability of each donor donating in the next three months.
Data is available from ...
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What is "noise" in observed data?
I am reading pattern Recognition and machine learning by Bishop and in the chapter about probability, "noise in the observed data" is mentioned many times. I have read on the internet that noise ...
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Correct way of calculating probability
I have some data which shows how many orders were made by a certain customer group that bought a certain product type:
And the same format but showing how many refunds were made:
I am trying to ...
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What is the difference between maximum likelihood hypothesis and maximum a posteriori hypothesis?
I am a student and I am studying machine learning. I am focusing on the concept of Bayesian learning and I have studied the maximum likelihood hypothesis and the maximum a posteriori hypothesis.
I ...
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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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Classification problem approach with Python
I am a Python beginner, just getting into machine learning and need advice on the approach i should use for my problem.
Here is an example of my data-set.
Where the RESULT is a corresponding INDEX ...
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In supervised learning, what does "Estimating $p(y \vert x)$" mean?
I read chapter 5.1.3 of Joshua Bengio's deeplearning book, which says:
supervised learning involve observing examples of random vectors $\textbf{x}$ and associated value or vector $\textbf{y}$ and ...
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Are real analysis and measure theory essential to learn for data science? [closed]
Some people say data scientists don't necessarily need to know real analysis and measure theory, but for others, real analysis and measure theory are very important for the undersdanding of kernel ...
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Problem understanding probabilistic generative models for classification
I am a student and I am studying machine learning. I am focusing on probabilistic generative models for classification and I am having some troubles understanding this topic.
In the slide of my ...
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How to model user choice probability: binary model vs multi class model
Let's say Morpheus has multiple users to offer colored pills(from an infinite set of colored pills), there are in total 3 unique colored pills(red, blue, green) Morpheus can offer. The trick is, ...
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