# Tag Info

1 vote

### Time Series clustering: clustering a dictionary of time series

Are you sure you want to do clustering? It sounds like you need to find consensus motifs among the wifi box users. It is easy to find them, see [a] [a] https://www.cs.ucr.edu/~eamonn/...

### Mathematical notation for number of samples of a predicted class that exceeds threshold of total number of samples

What i understand is you are doing is Bootstrap classification In general, What is Bootstrap Classification? Suppose you are doing Logistic Regression, (meaning a binary classification problem.) You ...
1 vote

### Where can I find pre-trained fully convolutional neural networks?

Some examples: image models from huggingface that can be used as image encoders, like clip-ViT from sentence-transformers in Pytorch/Tensorflow in Tensorflow/PyTorch you can manipulate CNN models by ...

1 vote

### Error in linear regression

Correlation is not sufficient to describe the distribution of the datapoints. You would need the full distribution in order to make predictions about the distribution of $y_{n+1}$ given $x_{n+1}$. ...
Accepted

### Why Logistic Regression is not a generative model?

The fundamental difference between Generative Model and Discriminative Model is, one is learning about $P(X,y)$ while discriminative model is learning $P(y|X)$ According to this definition, ...
1 vote
Accepted

### What type of prediction model will be suitable in this case?

This is a multidimensional problem in which the predictor variables $v_{ijn}$ have three indices: $i$ for subjects/individuals; $j$ for variables/measurements; $n$ for tissue samples. The variable ...
Accepted

### Dealing with very small and unbalanced data

Train/test split sounds like a bad idea indeed. I'd try jackknife resampling/LeaveOneOut cross-validation for this case.

### What is the main purpose of Feature Selection?

Allow me to be a contrarian and say that feature selection is overrated. My post here discusses feature selection when features are correlated, but the same bias-variance argument applies to ...
1 vote

### How to make a Neural Network(NN) learn when it is an input to an non-differentiable function?

Answering the general question, using back-propagation and gradient-based optimization algorithms is not the only possible way. There is a whole family of derivative-free optimization algorithms and ...