# 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.

4,265 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
12 views

### Spectral graph convolutional network, re-assigning indices

This is a silly question for whom is familiar with the theory. I came across few papers that use a particular definition of convolution, designed to work with graphs, for example see section 2.1. of ...
38 views

325 views

### How to handle Zeros in dependent variable in Multiple Linear regression

I am totally new to machine learning (and to this platform too) and was trying to implement Multiple linear Regression to improve my ranking algorithm. I have a data-set which have the following ...
160 views

### Number of observations in a node in XGBoost

I understand how the cover is calculated in XGBoost, the sum hessian at that node. For the root node of tree 1 for binary logistic, it becomes n(.5)(1-.5) with base score as 0.5. The cover at root ...
71 views

### How to handle machine learning inputs that should be considered as set of vectors, but whoes interpretation is order invariant (order agnostic set)

Basically wondering best practices for input modeling and ML algorithm type(s) for inputs that essentially model samples that are a bag/set of "sub-objects", so order does not matter. Think of the ...
205 views

### General PCA optimization problem

I was looking at the PCA optimization problem, which is finding a matrix $U \in \mathbb{R}^{d\times n}$, $n \le d$, that solves the problem $$\max{tr(U^TCU)},\ \ \ s.t. U^TU = I,$$ where $C$ is the ...
84 views

### Why use separate trees for each class in multi-class gradient boosting?

Gradient boosted decision trees can be used to solve multi-class classification problems. Friedman (2001) fit $K$ trees on each iteration—one for each class. Multiple GBM implementations also follow ...
53 views

### Find Feature Weighting in Deep Learning

If I train a deep neural network on standard tabular data (csv file etc. with labeled features) is there a good way to gauge how important each feature is in a particular new instance's prediction ...
101 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 ...
184 views

### Bias Variance decomposition derivation question/proof (from Wikipedia)

I have a question on this derivation of the bias-variance decomposition. At some point they have this part of the expression --> $\mathbf{E}[2y\hat{f}]$ and they say that $\epsilon$ and $\hat{f}$ are ...
343 views

### Multinomial Logistic Regreesion with Lasso penalty in R

I am applying regularized logistic regression (in R) to the handwritten digits data set. I have fitted a logistic multinomial model with lasso penalty to the training data. I am asked to obtain the ...
305 views

### Combining classification and anomaly detection

I want to build a system, that can classify known classes in a supervised way and at the same time tells if there is a new anomaly class it has not seen before. The user can then label that unknown ...
87 views

### Sampling a test set from global spatial data

The basis of testing the accuracy of any machine learning algorithm is to test the trained algorithm on data that it has never seen before. The usual approach to sample the test set is to just ...
81 views

### “Data beats hardware and algorithms in neural nets” paper?

I'm trying to track down the citation information for an article. The paper concerned itself with the recent explosion in successful applications of neural networks, and whether this was cause by ...
49 views

### Is it valid to calculate propensity score for each treated individual separately?

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 ...
842 views

### Are XGBoost probabilities well-calibrated?

In general, can you say anything about how well are the probabilities returned by XGBoost are calibrated? Is it true that, because XGBoost directly optimizes log-loss, probabilities are generally well-...
235 views

### Voting between classifiers : How to prove it works?

Assume m independent binary classifiers with probability $p$ to be correct $p>0.5$. Show that the probability of a voting, e.g. decision is made by the majority of classifiers is correct with ...
112 views

### Assessing correlated predictions

Let's assume we have a prediction algorithm (if it helps, imagine it's using some boosted tree method) that does daily predictions for whether some event will happen to a unit (e.g. a machine that ...