Unanswered Questions
3,378 questions with no upvoted or accepted answers
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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 ...
CommunityBot
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9
votes
1
answer
229
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What is Better for Prediction Error: Covariance Penalties or a Test Set?
I'm reading Computer Age Statistical Inference by Efron and Hastie, two statisticians I have a lot of respect for.
Section 12.3 discusses Mallows' $C_{p}$, Akaike's information criteria (AIC), and ...
CommunityBot
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9
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In sports modelling, are hot simulations better or cold simulations?
I'm thinking here largely of the context in which someone has an Elo rating model for a particular sport.
To calculate things such as how often the team makes the Finals series, or wins the ...
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Are there any General Proofs on Genetic Algorithms?
Are there any general proofs or theorems relating to "genetic algorithms"?
I have been reading about a theorem in math called the "Schema Theorem" - this theorem is one of the ...
8
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1
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809
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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 ...
CommunityBot
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What is the intuition for testing seasonal difference with OCSB test and its correct application?
I have daily time series data of a shop's revenue. Now I would like to test for seasonal differencing with the OCSB test originally intrduced in (Osborn et al. (1988): Seasonality and the Order of ...
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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 ...
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Including feature-dependent priors on output class, in bayesian logistic regression
When doing logistic regression with data $D_N = \{(x_i, y_i)\}_i^N$ with $x_i \in \mathbf{X}^N$ (each data point has N features) and $y_i \in \mathbf{Y}$ being assigned output classes, in a Bayesian ...
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prediction interval of a new prediction out of a weighted linear regression model
This question is based on the example 11.1 out of the book Applied linear statistical models of Kutner, Nachtsheim, Neter and Li. You can find the data here.
First they calculate a simple linear ...
- 84k
7
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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 \...
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7
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1
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Improve precision/recall for class imbalance?
Trying to get better precision/recall for both classes ... any tips?
I have heterogeneous features [a few num vars, a few cat vars, and 2 text vars]
Target is a binary classification w/ class ...
CommunityBot
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138
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Time series predictions with LSTM
I have collection of TEC data.My data sample for example the day1,day2,day3,day4.
Case1:
I have the following task to do: Training by the consecutive 3 days to predict the each 4th day. Each day data ...
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140
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Given a multiple-choice survey, test the hypothesis that choice i is more common in the population than choice j
I have a survey with m possible choices, only one answer allowed for respondent. Let $n$ be the sample size, and $\tilde{p}_k=\frac{n_k}{n}$, $k=1,\dots m$, the observed frequencies for each choice. ...
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Posthoc pairwise test on the output of a linear mixed-model (MATLAB or R/Python)
I have observations from three groups of participants (A, B, C), that each represent ratings on three different measures (measure 1, 2, and 3). I would like to know if it is the case that, regardless ...
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Why SVM with gamma='scale' for RBF kernel works so well?
The intuitive explanation for the gamma parameter of the RBF kernel in SVMs is the following:
Intuitively, the gamma parameter ...
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