# Questions tagged [variance]

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### How to get variance for regression tree fit?

Suppose the true function is a tree such that: $$f(x)=\sum_{j=1}^{J}b_j I(x \in R_j)+e_i$$ where $b_j=E(y|x \in R_j)$ ,$E(e_i)=0$ and $R_j$ as terminal node. Suppose we got a fit for this tree via ...
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### Generating discriminative features and addressing feature variations across different datasets for same set of data variables?

I am trying to build a classification-based machine learning model using efficient feature selection methods that predict the label of a dependent variable based on few independent variables. The ...
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### How do I prevent infinite variances/standard deviations in my variational autoencoder?

I am working on a project with a variational autoencoder (VAE). The problem I have is that the encoder part of VAE is producing large log variances, which leads to even larger standard deviations, ...
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### Is there R functions that allow to test for overdispersion when fitting a model with survey design?

I realized I need to use the package survey to be able to include sample weights in my regression analysis. Initially, I wanted to use a negative binomial regression on each one of my outcomes as ...
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### dividing Mean by standard Deviation meaning

I have played around with logistic regression a little using movement data intervals that are prelabeled as either resting or active. I now found that if I divide the mean movement of the individual ...
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### Generalization error problem on training set

Training data: $\mathcal {T} =\{(2,1),(3,2),(4,6),(0,0),(1,1)\}$ you already computed a predictor for the output using linear regression by least squares, where you used the first 3 samples as ...
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### KNN Variance using a high value of K and cross-validation

it has come to my understanding, that a value of K=1, gives a high variance because we are only using only one data point, hence we are very likely to model the noise in that training example. Bias: ...
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### Lower Variance vs. Higher Validation Scores

So I'm trying to compare between two models, say model(1) has training accuracy of 90% and validation accuracy of 86%, while model(2) has training accuracy of 87% and validation accuracy of 85%. Now, ...
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### Reducing (Variance) | the gap between my weights

I have ML ready samples. And each sample has a weight. The weights distribute between [0-1] My problem arise because there are a lot of samples which are ...
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### Given a regression based model with many feature variables; what tools would you utilize to figure out which feature variables add the most variance?

Given a hypothetical dataset {S} with 100 X feature variables and 10 predicted Y variables. X1 ... X100 Y1 .... Y10 1 .. 2 3 .. 4 4 .. 3 2 .. 1 Let's say I want to improve the accuracy of Y1. I am ...
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### How to find a probability distribution the parameters of which do not impact each other like mean and variance in normal distribution do?

I need to find a probability distribution to fit my data. My data has two important features, duration and activity count. Duration means how long one sequence lasts and activity count means the ...
33 views

### Odd results on bias-variance tradeoff assessment

I am running a bias-variance tradeoff assessment on ten regression models of increasing complexity (linear to x^10), but my results do not satisfy the MSE = Bias^2 + Variance + True Error relationship....
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### Why is the variance of my model predictions much smaller than the training data?

I trained a GRU model on some data and then created a bunch of predictions on a test set. The predictions are really bad, as indicated by a near zero R2 score. I notice that the variance of the model ...
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### How does C have effects on bias and variance of a Support Vector Machine?

The minimization problem for SVM can be written as- $$\overset{\text{min}}{\theta} C\sum_{i = 1}^{m}{[y^icost_1(\theta^Tx^i) + (1-y^i)cost_0(\theta^Tx^i)]} + \frac12\sum_{j = 1}^n{\theta_j}^2$$ Now, ...
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### Why is the variance going down so much in this weight initialization problem(using pytorch)?

first look at this example >>> x = t.randn(512) >>> w = t.randn(512, 500000) >>> (x @ w).var() tensor(513.9548) it makes sense that ...
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### Is it correct to say that a high variance feature will be important to my model?

Analysing 3 features I was faced with the following situation: Two of them have almost no variance and the last one have a greater variance than the other (almost two times). Is it correct to assume ...