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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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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4answers
3k views

What problem does oversampling, undersampling, and SMOTE solve?

In a recent, well recieved, question, Tim asks when is unbalanced data really a problem in Machine Learning? The premise of the question is that there is a lot of machine learning literature ...
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896 views

How does a Relevance Vector Machine (RVM) work?

Relevance Vector Machines (RVMs) are really interesting models when contrasted with the highly geometrical (and popular) SVMs. In the light of a question like How does a Support Vector Machine (SVM) ...
14
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2answers
9k views

Interpreting negative cosine similarity

My question may be a silly one. So I shall apologize in advance. I was trying to use the GLOVE model pre-trained by Stanford NLP group (link). However, I noticed that my similarity results showed ...
12
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0answers
4k views

Backpropagation on a convolutional layer

Online tutorials describe in depth the convolution of an image with a filter, etc; However, I have not seen one that describes the backpropagation on the filter (at least visually). First let me try ...
12
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0answers
795 views

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 ...
11
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0answers
1k views

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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0answers
2k 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 ...
9
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2answers
1k views

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, ...
8
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0answers
405 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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0answers
1k views

Post processing random forests using regularised regression: what about bias?

I have been playing around with post processing the results of the random forest for regression machine learning algorithm in order to try and do better than the default mean of all trees prediction. ...
7
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1answer
81 views

inferring most important features

Given a set of $n$ instances. For each instance I have a feature vector consisting of $m$ (numerical) features ($x_1$, $x_2$,...,$x_m$), n>>m. Moreover, for each instance I have a numerical score $y$ (...
7
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1answer
197 views

Why does entropy as error measure leads to overfitting?

This post on KDnuggets quoting the post by John Langford states that entropy and mutual information when used as error measures can lead to overfitting. Could you go into more details on this?
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763 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) ...
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0answers
586 views

How to pick length-scale bounds for RBC kernels in Gaussian Process Regression?

I am trying to fit GP regression models to several thousand $x, y$ pairs independantly. I am using Python's sklearn implementation with a constant kernel plus an RBF kernel plus a white noise kernel. ...
7
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0answers
308 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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0answers
611 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
4k 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
118 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 ...
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0answers
2k views

Confidence interval of precision / recall and F1 score

To summarise the predictive power of a classifier for end users, I'm using some metrics. However, as the users input data themselves, the amount of data and class distribution varies a lot. So to ...
6
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0answers
3k 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 ...
6
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0answers
377 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
576 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
649 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
476 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 ...
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
364 views

Inter-rater agreement of a gold standard dataset - a ceiling for reliable evaluation of algorithms?

In my field, a dated gold standard dataset is used to track progress in algorithm development. Now when the state-of-the-art algorithms obtain higher correlation than is the inter-rater agreement of ...
6
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2answers
5k 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?
6
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1answer
162 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 ...
6
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1answer
378 views

Unsupervised pre-training for Reinforcement Learning

Since the advent of many unsupervised learning methods, as a pretraining step for the main supervised task (mostly under the name of Deep Learning), it shouldn't be strange to ask, what is the current ...
6
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0answers
784 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
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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1answer
6k 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?
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94 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 ...
5
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1answer
132 views

Time Series Regressor Selection

I am interest in a (multivariate) algorithm to identify relevant regressors (which are itself time series) to forecast a time series of interest. The question is worded in general terms because this ...
5
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0answers
70 views

Asymmetric or unequal misclassification costs in random forest

I have a general question about asymmetric costs. In machine learning problems, there are times when the cost of a false positive is different from the cost of a false negative. Accordingly, models ...
5
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0answers
37 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
251 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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0answers
191 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 ...
5
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0answers
523 views

Why no one talks about stochastic conjugate gradient descent?

As is known to all, stochastic gradient descent is a popular optimizer in machine learning. There have been many variants of SGD. However, it has come to my attention that no one talks about the ...
5
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0answers
151 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 ...
5
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0answers
6k 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 ...
5
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0answers
2k views

How does XGBoost/lightGBM evaluate ndcg metric for ranking

I am currently running tests between XGBoost/lightGBM for their ability to rank items. I am reproducing the benchmarks presented here: https://github.com/guolinke/boosting_tree_benchmarks. I have ...
5
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1answer
317 views

Is that possible to distill the knowledge of a stacked ensemble model?

There is a famous paper "distilling the knowledge in a neural network" from Hinton about training a small NN to represent a large deep NN. Is that possible to do the same thing for a stacked Ensemble ...
5
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0answers
289 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 ...
5
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0answers
237 views

Ensemble learning with time-varying covariates and effects

We are interested in replicating several duration studies in the literature using ensemble learning methods. After some experimentation, we opted for random survival forests (Ishwaran et al. 2008) for ...
5
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1answer
2k views

Using Rolling Forecast Origin Resampling in R for Neural Network Time Series

I am new to time series prediction and forecasting with neural networks and am having trouble with cross validation. I am fitting a multivariate time series. I have 236 monthly observations. I am ...
5
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0answers
301 views

Is there an appropriate order to apply bagging and filter feature selection?

I'm training a (regression) learner on a $p \gg n$ problem, including bagging and filter feature selection (information gain). I'm in doubt though regarding the order of the procedures: Apply the ...
5
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0answers
3k views

Bagging of xgboost

The extreme-gradient boosting algorithm seems to be widely applied these days. I often have the feeling that boosted models tend to overfit. I know that there are parameters in the algorithm to ...
5
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0answers
8k views

True positive, false negative, true negative, false positive definitions for multiclass-multilabel classification?

I'm trying to apply some evaluation metrics to several clustering methods. I thought that I knew them basing on the multiclass confusion matrix, considering the rows as the actual classes and the ...