Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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6 views

Using federated learning FedAvg with more workers provide lower accuracy

I use federated learning FedAvg to predict accident severity on the roads. I have 3 workers in total and each worker has a different number of samples randomly, e.g., worker-1 has 10K samples, worker-...
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21 views

How to prove that the convexity of Generalized Linear Models?

Refer to question: Does log likelihood in GLM have guaranteed convergence to global maxima? The top answer said that one can prove $\frac{d A}{d \theta}=\mathbb{E}[\phi(x)]$ $\frac{d^{2} A}{d \theta^{...
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28 views

Random Forest with train AUC = 1 and test AUC = 58%

I'm trying to understand why my train AUC = 1 while my test AUC is near 58% using random forest. Context: You are trying to sell a product, and you have historic data about the purchases/noPurchases ...
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11 views

The most important matrices for evaluating a predictive model for customer churn

My questions is as above. What are the most important matrices (f1, precision, recall...etc) that I need to prioritize my work to improve for evaluating how good a model predict customer churn and the ...
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2answers
42 views

Disadvantage of precision at k

Suppose 10 documents were retrieved (rectangle with black color is relevant document). In the following table, Precision @ k is calculated. P@10 or "Precision at 10" corresponds to the ...
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1answer
25 views

When should an embedding layer be used? How big should an embedding be?

I am currently in the process of learning about seq2seq autoencoders for a task involving sentence embedding (samples are sentences, with words represented as integers in a vocab of size $n$). In the ...
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20 views

Graphical representation of Bayes decision boundary

Here is my problem statement: Let $X=(X_1,X_2)∈[0,1]×[0,1]$ and $Y∼Bernoulli(p=X_1⋅X_2)$. Plot the Bayes decision boundary ${(x1_,x_2):P(Y=1|X=(x_1,x_2))=0.5}$ and indicate the regions in $[0,1]×[0,1]$...
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18 views

ML Algorithm for Online Sequence Classification

I am writing a program to classify API call sequences at runtime. At the moment I am using pytorch as my ML framework. Initially I thought this could be accomplished with an LSTM network, but from ...
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1answer
30 views

Can a binary output model with auc 0.5 be perfectly calibrated?

I am reading up on model calibration and I stumbled upon this article. To quote: We can have a perfectly accurate model that is not calibrated at all and, on the other hand, a model that is no better ...
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15 views

Suitable (statistically sound) predictive methods when dealing with limited data that was not generated through any kind of controlled experiment?

I was just reading the Reddit thread "My issue with data science" in r/datascience. One of the main points made in the thread is that prediction is fundamentally a different game to causal ...
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8 views

What is the function of the denominator in the likelihood estimation equation (used in naïve bayes classifiers)?

I understand that the likelihood is calculated/estimated by looking at the number of instances where a certain feature and class occur together divided by the total instances of that class. However, I ...
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25 views

Bayesian inference of stochastically evolving model parameters

I have a question related to self-calibration in radio interferometry, but I will try to phrase it as generic as possible. I have a set of data points $$D = \{ d_{0, t_0}, d_{1, t_0}, \cdots, d_{M, ...
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24 views

Probability of disease given sensitivity and specificity of test and prevalence [closed]

A disease that occurs in 1% of the population has a test with a 3% false positive rate and a 6% false negative rate. If the test comes back positive for a random member of the population, what is the ...
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10 views

calculate the parameters from a confusion matrix [closed]

am asking how we can calculate the parameters from a confusion matrix: Confidence Interval = No information Rate = P-value = Mcneamar’s P-value=
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5 views

What is a good metric to use in order to evaluate a performance of DNN

I need to compare the performance of a basic feedforward regression type DNN for different combinations of independent variables for the same dependent variable. Can I just compare the versions of the ...
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8 views

How to create differentially private synthetic or noisy dataset?

I can view my original dataset as points in n dimensional space, how do I create a differentially private synthetic dataset for such a dataset. You can also think of it as having a dataset with m ...
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21 views

How random forest work? (Manual calculation)

I just learned about the concept of random forest (how the algorithm works) and I am confused on two things: If I use majority vote to classify something and I have 52 trees. I get result like this: ...
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2answers
115 views

Why don't we estimate the prior in a Naive Bayes' classifier?

I'm currently studying the textbook Introduction to Machine Learning 4e (Ethem Alpaydin) the brush up on my ML basics and had a question regarding a part w.r.t. using the Naive Bayes' classifier in ...
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1answer
32 views

K-Means output the similar to each other cluster

I am trying to run K-Means on my data set of house price prediction problem. After running it, the output of the model seems wrong because the graphs look the same as each other. This is my code: <...
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1answer
324 views

Why is it bad if the estimates vary greatly depending on whether we divide by N or (N - 1) in multivariate analysis?

I'm currently going through the textbook Introduction to Machine Learning 4e (Ethem Alpaydin) to brush up on my ML basics and had a question regarding the chapter on multivariate methods. More ...
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2answers
41 views

Getting the probability or accuracy from each prediction in random forest?

I have a random forest model to predict MLB player's fantasy points. I have the MSE and R^2 score, but I would like to know the accuracy of each individual prediction as opposed to the accuracy of the ...
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15 views

Packages in python for logistic regression on an unbalanced panel data [closed]

I am new to building models in Python, any help will be appreciated. I want to build a logistic regression model (with fixed/random/mixed effects) for an unbalanced panel data I have. The data I have ...
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1answer
55 views

How does bagging reduce variance?

I read this answer. Was still unable to understand how bagging reduces variance. Is there any other way to explain it mathematically to a newbie ? Edit Can anybody explain me this excerpt from the ...
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23 views

Is this the correct hypothesis function for logistic regression?

I was reading a popular article on adversarial training. https://adversarial-ml-tutorial.org/linear_models/ It says, In this case, rather than use multi-class cross entropy loss, we’ll be adopting ...
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12 views

What exactly is explained sum of squares, and why do we focus on minimizing error rather than maximizing explained variability?

In the third of my questions about decomposing the total sum of squares, I want to focus on the sum of squares of the regression. I can make sense of what the sum of squares of the residuals means: ...
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12 views

Total sum of squares decomposition and Brier score

Building on my previous question, we also can use square loss when we do classification problems (probability of class membership, really). When we use square loss in a classification problem, it’s ...
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1answer
20 views

Is it reasonable to extract split-point locations of an RF model?

If I build a random forest model (classifier) and display the results in a partial dependence plot (PDP) and observe a clear abrupt change. Is it then reasonable to extract the split-points of every ...
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1answer
18 views

Why does the training cost of my model decrease, then suddenly increases, never going down again?

I'm trying to train an RNN, and I'm encountering difficulties with the cost as training progresses. I've had success with my code with previous instances of my code (basically, smaller systems). ...
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4 views

Do the selected hyperparameters from the inner loop of a nested k-fold cross validation still induce bias?

I think the nested k-fold cross validation does not really provide an unbiased estimate of performance. If I understand correctly, in the image below, we will first perform a 10-fold cross validation ...
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14 views

Calibration measure for classification with linear slope

I would like to know if there is a measure for calibration, in binary classification case, that is global, and not only a visual one, like reliability/calibration plot/curve. In particular, in another ...
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1answer
38 views

Proving that a function is not a kernel function

The function is defined as $k(x,x')=||x||$ Norm in Hilbert Spaces can be defined as $||x||= \sqrt{x^Tx} $. I am not sure about the feature map of this function that how will it be and I am positive ...
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31 views

What is the name of the probability distribution of ROC-AUC when training machine learning models?

When training ML models like neural networks they are random initialized. That has the effect that the results (ROC-AUC for example) are influenced by random effect. When I train them multiple times ...
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18 views

What is “conditional” about conditional GAN?

I was recently reading the conditional GAN paper https://arxiv.org/pdf/1411.1784.pdf In the conditional GAN paper, the author try to approximate two functions $D(x|y)$ and $G(z|y)$, where $x$ ...
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1answer
26 views

Is a high learning rate irrelevant when dropping the first or last tree in a GBDT with 100 trees?

Suppose we've trained a GBDT model with 100 trees with a fairly high learning rate. Consider two cases: We drop the first tree in the model We drop the last tree in the model We then compare models ...
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6 views

Data augmentations to mimic natural variations present satellite imagery

I'd like to apply some machine learning algorithms to satellite imagery that we've collected, but I want to encourage invariance to factors such as sunlight intensity, time of day, atmospheric ...
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1answer
83 views

Why can we suppose $\epsilon \sim \mathcal{N}(0,\sigma^2)$? [duplicate]

Suppose we want a regression of a function $f(x)$. Suppose $r = f(x) + \epsilon$. Why can we suppose that $\epsilon \sim \mathcal{N}(0,\sigma^2)$? What is the advantage of such supposition?
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1answer
47 views

explain the cost function of scalar vector machine (SVM)

I am unable to understand the above cost function. The two possible outputs ($y$) are $-1$ and $+1$. As far as i know, $x_i$ values are individual training example values, $y_i$ are actual true values ...
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1answer
22 views

Number of model DoF in XGBoost regressor?

I'm using XGBoost (python) to solve a regression problem. I instantiate the model like so: import xgboost mod = xgboost.XGBRFRegressor() I would like to extract ...
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8 views

How choose formula in the Generalized Additive Model using Thin Plate Splines?

I am using mgcv to train GAM. For example, suppose we have a Dataset with 3 Features: x, y and z. When should I use a Thin Plate Spline for each variable? When should I use a Thin Plate Spline with ...
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20 views

Semantic segmentation network fails with greater image resolution

I am training a U-Net for (binary) semantic segmentation on 3D images. The network seems to work reasonably well when the voxels are downsampled (by simply summing over values of neighboring voxels). ...
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1answer
13 views

Question about the right inverse method in a GLM of order 2

I have taken a course in regression analysis. I learned that the equation $\beta =(X'X)^{-1}X'y$ can be used to find the weights in a linear model. When learning about GLMs, I came across this formula ...
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9 views

In what order to perform data preparation methods? [closed]

Is there a best practice in the ordering of techniques used to preparing data? That is to say would any technique negatively impact another if preformed first? Techniques such as: 1.Imputing missing ...
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11 views

Non - convergence of loss function in Neural Networks with a step activation function [duplicate]

In the below lecture by Professor Patrick H. Winston on Neural Networks: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/...
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2answers
53 views

Would there be any advantage in using Neural Network regression instead of classification?

I have developed several models to predict a given output (let's say the output is something like "test score", that goes from 0% to 100%), based on several variables that influence that ...
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1answer
30 views

Separable data isn't generalized by any majority

I have a dataset with 12k samples where each sample is a feature vector of length 4096. The task it to classify the samples into 12 categories. I fitted a network with 2 fully connected layers on the ...
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1answer
23 views

Vocabulary: name for a variable known but ignored

Suppose I have groups (A, B, ...) and in each group I have several inputs and outputs (of the same type). I want to model each output from the inputs, in that case, the group variable is known, but I ...
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20 views

How to programmatically differentiate between MCAR (Missing Completely at Random), MAR(Missing at Random), and MNAR(Missing Not at Random) in python

I found the following code in R. Im not sure how much does it serve this purpose. But I want to implement this in python. How does this mostly convert to?? I also want to differentiate between all ...
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51 views

Custom cost function in linear regression using gradient descent [closed]

I would like to use custom cost function in following form: $$ J(\theta) = \|X\theta - Y\|^2 + \|Y\theta^\mathrm{T} - X\|^2 $$ my goal is to make $\theta$ matrix as close to orthogonal as possible ($\...
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3answers
116 views

Mathematical/Statistical Assumptions Underlying Machine and Deep Learning Methods

I was recently reading a discussion amongst mathematicians/statisticians about machine and deep learning, and how they are applied by non-mathematicians/statisticians. The argument was that these ...
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1answer
25 views

Dealing with Different Incident Rates in Training vs Test Sets

I am training a binary classification model with about 8000 observations in the training set and 500 in the test set (sets are dictated to me so I can't modify the split). In the training set the ...