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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35
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1answer
2k views

Link Anomaly Detection in Temporal Network

I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
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1answer
3k views

How can I get feature importance for Gaussian Naive Bayes classifier?

I have a dataset consisting of 4 classes and around 200 features. I have implemented a Gaussian Naive Bayes classifier. I want now calculate the importance of each feature for each pair of classes ...
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Bayesian Q-learning

Suppose that, for every state $s$, there is a set of actions $\mathcal{A}(s)$ that can be chosen in that state. Let $Q(s, a)$ denote the expected utility of choosing action $a \in \mathcal{A}(s)$ in ...
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Rademacher complexity of logistic regression

Consider logistic regression. We have the logistic loss function, $\phi: R\rightarrow [0,1], \phi(u)=\log(1+\exp(-u))$, which is Lipschitz, and we have the linear function class $F=\{f_w:R^d \...
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2answers
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Scaling the backward variable in HMM Baum-Welch

I am just trying to implement the scaled Baum-Welch algorithm and I have run into a problem where my backward variables, after scaling, are over the value of 1. Is this normal? After all, ...
9
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1answer
1k views

Applying machine learning techniques to panel data

I have a panel data in which I observe 1500 companies and many individuals work for those companies for multiple periods. I have explanatory variables at both individual (e.g. race, age, education) ...
9
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0answers
456 views

Can Frank Harrell's method be used to obtain optimism-corrected regression coefficients?

I used a regularized (LASSO) Cox regression to estimate relapse times of patients and used Frank Harrell's bootstrapping method to obtain an optimism-corrected performance estimate of my model. ...
8
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1answer
97 views

Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?

I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
8
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0answers
155 views

Understanding equation used by Hastie et al

I am trying to recreate FIGURE 3.6 from Elements of Statistical Learning. The only information about the figure is included in the caption. I am not clear on what the equation on the Y-axis means ...
8
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1answer
286 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
7
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1answer
9k views

What does “def” above an equals sign mean?

I am reading this: https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf and on equation (17), there is a def on top of the equal sign. What does this mean?
7
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1answer
126 views

Feature Engineering : combine a categorical Feature and a continuous Feature

When we analyze data , we can observe several variables that may contain mutual information. For an example , There can be a binary variable such as Y=Have you ever smoke ? And then there will be a ...
7
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490 views

what is the mistake of convergence proof in Adam

Sashank J. Reddi et. al in their paper "On the convergence of Adam and beyond" say that, Adam's proof of convergence as stated in original paper is wrong. More than that, they point out that the value ...
7
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0answers
7k views

Deriving linear regression gradient with MSE

So I've been tinkering around with the backpropagation algorithm and to try to get a better understanding of how it works and my calculus is quite rusty. I've derived the gradient for linear ...
7
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1answer
618 views

What is the utility/significance of PAC learnability and VC dimension?

I've been reading Shalev-Shwartz & Ben-David's book, "Understanding Machine Learning", which presents the PAC theory in its Part I. While the theory of PAC learnability does appear very elegant ...
7
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318 views

Reinforcement *Model* Learning

Classical reinforcement learning (Q- or Sarsa-Learning) can be extended with models of the environment. These models are usually transition tables that contain the probability of arriving at a ...
7
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1answer
526 views

Deep Learning vs Structured Learning

I am interested in the differences between using large, deep learning networks vs Probabilistic graphical models (PGMs), like Random Field models, for structured learning (e.g. on images, or labels of ...
7
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2answers
6k views

Is it ok to get negative Cosine Similarity using LSA?

I am getting negative cosine similarity value between two documents in Latent Semantic analysis. How should it be treated?
7
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0answers
694 views

Machine learning with ordered labels

The usual method for adapting binary classifiers like various SVMs to multilabel data is one-vs-all, which assumes that labels are independent and in case of a prediction error we don't care what ...
7
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1answer
5k views

Unscented Kalman filter-negative covariance matrix

I have recently started working on the unscented Kalman filter. I coded the numerically stable version (i.e., square root Kalman filter) and used MATLAB for implementing. In the final update step, ...
6
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0answers
43 views

Interactions in Multiple Linear Regression (Divide Vs Multiply)

My question is about the difference (in general) between the interaction terms $x_1x_2$ and $x_1/x_2$ in multiple linear regression. Suppose you are performing multiple linear regression and you have ...
6
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0answers
107 views

Moments of $Y=X_1 + X_2 X_3 + X_4 X_5 X_6 +\cdots$

The $X_i$'s are i.i.d. and $X$ denotes any of these random variables. We assume here that $|E(X)|<1$ to guarantee convergence. I am interested in particular in the third moment $E(Y^3)$. For the ...
6
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0answers
75 views

How to combine noisy and noise-free datasets to train a model

Overview Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
6
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1answer
378 views

How to predict routes using clustering data

I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
6
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0answers
79 views

Choosing the number of hidden layers and nodes in a Deep Belief Network

What are the recent advances and current best practices in choosing the number and size of stacked Restricted Boltzmann Machines in Deep Belief Networks ?
6
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0answers
162 views

When is there a free lunch?

The no free lunch theorem (NFL) states that Theorem (Wolpert and Macready 1997) Let $A$ be any learning algorithm for the task of binary classification with respect to the $0−1$ loss over a ...
6
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0answers
7k views

Change image input size of a pre-trained convnet

maybe this question will sound a bit as a newbie one but I'd like to have some clarification. I'm using a VGG16-like convnet, pre-trained with VGG16 weights and edited top layers to work with my ...
6
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1answer
507 views

How do bias, variance and overfitting relate to each other?

I'm quite new to Machine Learning, and after reading about the bias-variance tradeoff and overfitting/underfitting, several questions raised in my mind: If I have a model with 15% error on train set ...
6
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0answers
801 views

Compatible Function Approximation Theorem in Reinforcement Learning

In the Compatible Function Approximation Theorem, the following condition is required to make the policy gradient to be exact $\nabla J(\theta) = \mathbb{E}_{\pi_{\theta}}\left [\nabla_{\theta}log\pi_{...
6
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1answer
712 views

Multi-label classification: Predict product category

I want to predict to which product category a product belongs. A total of 400k products need to be translated from the old (less refined) to the new product category tree. (E.g. alarm clock used to ...
6
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0answers
2k views

Which standard deviation of the cross-validation score?

When doing cross-validation for model selection, I found there are many ways to quote the "standard deviation" for the cross-validation scores (here "score" means an evaluation metric e.g. accuracy, ...
6
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0answers
1k views

Dealing with auxiliary random variables for Mean-Field Variational Inference in Bayesian Poisson factorization

I am studying as a part of a class assignment a recent paper on Poisson factorization. Some points of the paper regarding the usage of some auxiliary variables are not clear to me. I would like to ...
6
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0answers
436 views

Any implementations of fully recurrent neural networks applied to reinforcement learning?

I've seen a single paper on the topic of adapting fully recurrent networks to a reinforcement learning setting, but according to google scholar its had no citations and no code has been released ...
6
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0answers
1k views

k-fold cross validation vs k times hold-out validation

I am facing the evaluation of a genetic programming algorithm. I am using the Proben1 cancer1 dataset to evaluate the models created by this algorithm. This dataset contains 699 samples, which is ...
6
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0answers
199 views

How to learn similarity of typed/attributed graphs?

I have a question for graph machine learning gurus :). For this project I'm working on, I need to be able to learn similarity between typed graphs. By typed I mean that every vertex and every edge of ...
6
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0answers
802 views

Classification of multiple time series and case level attributes

I'm pretty new to machine learning so wondering whether someone can help check my thinking or point me in the right direction! I need to create a classifier which can predict an outcome for a person ...
6
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0answers
139 views

Graphical nominal model

Suppose I have a set of $k$ matrices. $$ \mathbb D = A_1,A_2,...,A_k $$ Each column of $A$ is categorical vector. $$ A = v_1,v_2,...,v_n $$ I want to find the mapping $$ f: A \...
6
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0answers
2k views

Maximum entropy classifier and sentiment analysis

I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
5
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0answers
46 views

What does non-linearity mean when using only binary predictors?

I'm working on a project that uses genetic data. The dataset has thousands of predictors that are all binary (does the person have a certain letter in a certain position in their genetic code: yes/no)....
5
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2answers
153 views

Noise in regression problems and ways to reduce it

In the theory of bias-variance decomposition for regression problems (this page is a very nice reference on this theory) noise is defined as $$\mathrm{Noise} = \mathrm{E}_{X,Y}[(Y - \mathrm{E}[Y|X])^2]...
5
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0answers
344 views

Understanding Object2Vec

AWS released an interesting feature as part of the SageMaker service called Object2Vec that lets you make an embedding for search out of pretty much anything: documents, users, products, ...
5
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1answer
115 views

XGBOOST objective function derivation algebra

I need some help please with the derivation of xgboost objective function. I am following this online tutorial (Math behind GBM and XGBoost) How do you go from here $$ loss = \sum_{i=1}^{n} \left( ...
5
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0answers
384 views

Difference between Shapley values and SHAP

The Paper regarding die SHAP value gives a formula for the Shapley Values in (4) and for SHAP values apparently (?) in (8) Still I dont really understand the difference between Shapley and SHAP ...
5
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1answer
748 views

How is TD(1) of TD(lambda) equivalent to Monte Carlo?

In Sutton and Barto's book about RL they say that the TD($\lambda$) algorithm is equivalent to Monte Carlo when $\lambda = 1$. I don't see how that is the case. They define the lambda return as: $$...
5
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0answers
43 views

Feature engineering for sheet music

I have a large dataset of digitized music scores that I'd like to use as input to a network. Initially, I'm looking to train networks to identify key signatures, tempo, dynamics, etc. from the raw ...
5
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0answers
455 views

“Hierarchical” Random forests?

Background I am using Random Forest to classify ~900 objects based on a large number (> 80) predictors. I split these 70:30 for training and testing. The overall model does fairly well, giving an ...
5
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1answer
109 views

Conterfactual estimation in machine learning model

There are various techniques to build counterfactual estimations of certain variables for linear models in observational studies. Some of those are based on comparing the change in the predicted ...
5
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0answers
236 views

Correcting Sample Selection Bias given actual Distribution

I have two datasets, both from the same population: The samples from the first survey are quite representative of the underlying truth. However, the second survey comes with a change in distribution ...
5
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0answers
163 views

A few questions regarding the practice of heterogeneous treatment effect analysis (a.k.a, interaction detection or subgroup analysis) methods

Imagine I am looking at a randomized experiment between a control and one or more treatment conditions. For example, I have a treatment that aims to get people out of debt. I randomize people to ...
5
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0answers
218 views

Quantifying uncertainty when fitting a statistical model to partial effects/dependencies from a random forest (or other machine learning model)

Question: I estimate the partial dependence of $y$ on one predictor in a fitted random forest (RF). I want to now fit a parametric model to this partial dependence. How can I estimate my uncertainty ...

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