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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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2 votes
0 answers
9 views

I’m stuck and this is starting to feel pretty convoluted, so I’ll try to be clear. What I have: A timestamped stochastic time-series (e.g. market prices). It’s noisy but when an event happens the ...
3 votes
0 answers
19 views

I’m trying to understand the common assumptions in machine-learning optimization theory, where a "well-behaved" loss function is often required to be both L-Lipschitz and β-smooth (i.e., have β-...
0 votes
0 answers
24 views

Its 2025, and yes I'm still using SAS EMiner's Decision Tree..... If anyone knows a modern freeware version that replicates the Interactive mode effectively (with controlling split cutoff values, a ...
0 votes
0 answers
23 views

I have 3 months of categorized bank transaction data and need to identify recurring cash inflows and outflows for lending risk modeling. Complications: 1. Income dates shift earlier when payday falls ...
2 votes
0 answers
38 views

My training data is mostly missing values for the feature that I know will be the most important variable. This missingness is semi-random. For example, I know the value is missing for this feature ...
0 votes
0 answers
23 views

I am listening to a lecture on soft margin SVM https://youtu.be/XUj5JbQihlU?si=b66SblRnw9mmczVU&t=2969 The lecturer says that the blue dot represents a violation of the margin. I don't really ...
3 votes
1 answer
102 views
+50

I've encountered the term "accuracy" used differently across several evaluation contexts, and I want to clearly understand their mathematical and conceptual distinctions using consistent ...
1 vote
0 answers
29 views

It is widely known that if you were to calculate the maximizer of the dual SVM program (denote as $\alpha^*$), then the primal minimizer of the hard-margin SVM program, \begin{aligned}&{\underset {...
0 votes
1 answer
51 views

I come from a machine learning background, however I am trying to learn more traditional data science. I have a dataset of vehicles and the target is the Breakdown Likelihood (1 to 3, 1 being lowest), ...
0 votes
0 answers
26 views

I’m building a regression model that predicts the final number of vehicles booked for a ferry trip. Each training row represents the state of bookings for a given trip N days before departure. Example ...
0 votes
0 answers
41 views

The first paragraph of the Wikipedia page for "data augmentation" seems to conflate two different meanings of the term. The more classical definition comes from Bayesian computation: ...
0 votes
0 answers
59 views

TVD-MI (Total Variation Distance–Mutual Information) has been proposed as a mechanism for evaluating the trustworthiness of judges (such as LLMs scoring code correctness or theorem validity) without ...
0 votes
0 answers
16 views

In the context of the TVD-MI (Total Variation Distance–Mutual Information) mechanism described by Zachary Robertson et al., what precisely do the indices (i, j) represent? Specifically, are (i, j) ...
1 vote
0 answers
37 views

I am working on a demand forecasting problem for ferry vehicle capacity. For each voyage, I have daily snapshots of the cumulative reservations from the opening date until departure day. So each ...
3 votes
2 answers
67 views

When using machine learning algorithms for regressions, I know that the prediction of the final model will be best when the features are within the ranges used for training, to avoid extrapolation. ...

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