# Questions tagged [variance]

The tag has no usage guidance.

123 questions
Filter by
Sorted by
Tagged with
24 views

### Comparing Systems Based on Their Variance

I am using simulations to compare two economic models and want to understand their impact on returns (i.e., the percentage change in prices). I have employed common random numbers for these ...
• 1
16 views

### Is always low bias and low variance desirable?

Assume we have two regression models M1 and M2 for a given data. Assuming M2 has lower bias and lower variance, would you always consider using this? This example shows that if the data is random ...
1 vote
82 views

### How do I compute and plot Bias and Variance of a classifier in Python?

I'm new to Machine Learning and I understand bias and variance in theory but I can't seem to find a single source that explains how bias or variance can be computed. I'd like to do it in Python and ...
• 113
7 views

### How to derive the formula 13 in the Xavier Initialization paper

How to derive the formula 13 in the Xavier Initialization paper Understanding the difficulty of training deep feedforward neural networks from the formula 6?
• 761
12 views

### i am trying to get the the variance of derivative of order 2. i only have the data of an EEG signal sampled at 128 Hz. is the below code correct

I basically want to implement equation 7 present in the added image ...
77 views

### Why the standard deviation of the BERT weight initialization is 0.02 by default

The purpose of weight initialization in the neural network is to keep the variance of calculation output in the layers to 1.0, and it depends on the calculations involved in the layers. Initializing ...
• 761
186 views

### What is the best practice to detect constant and nearly constant data over time?

I'm experimenting with the characterization of data over time. I generated some synthesized data with certain periodic patterns over time every 5mins (granularity of 5mins= data generated with the ...
• 400
10 views

### Cross-Entropy Loss for Prediction Distribution

I'm trying to adapt an example from the Neural Tangents Cookbook which uses a variant of Mean Squared Error (MSE) loss suited for Regression prediction distributions given by Mean and Variance as ...
54 views

This question is from a uni module about machine learning. I'm a bit stuck as I can't relate it to the bias-variance trade-off, to me the question implies all models have something to do with the ...
62 views

### For standard deviation's formula, why does division by sample size come before square rooting?

Why is this used to calculate a sample's standard deviation: $s=\sqrt{\frac{1}{N-1}\sum_{i=1}^N(x_i-\bar{x})^2}$ and not something like: $s=\frac{1}{N-1}\sqrt{\sum_{i=1}^N(x_i-\bar{x})^2}$ I ...
179 views

### Understanding bootstrapping in bias variance decomposition

I was going through bias and variance tradeoff article and it makes use of bias_variance_decomp function from mlxtend library. ...
• 199
46 views

### variance explained by model

This is a question for beginners. edited 19/11. I am really confused by the term variance and so many other variants. For example, the figure below shows the variance of two models to compare. Is ...
71 views

### Relation between MSE and variance of data

I am having a hard time understanding how to compare results of MSE and variance of data to eachother. I understand that MSE is used to calculate how far off data points are from a prediction, say you ...
161 views

### Proof for MSE = Var + Bias2

I am trying to prove the equality of $$\rm MSE=Var+Bias^2$$ but obviously I got something wrong as they don't equal in my calculation: So here is the example. I use monte carlo to estimate this ...
• 133
1 vote
84 views

### Whether to use LDA or QDA

I'm trying to determine whether it's best to use linear or quadratic discriminant analysis for an analysis that I'm working on. It's my understanding that one of the motivations for using QDA over LDA ...
• 11
173 views

### VIF Vs Mutual Info

I was searching for the best ways for feature selection in a regression problem & came across a post suggesting mutual info for regression, I tried the same on boston data set. The results were as ...
368 views

### Why and how Variational Inference underestimates variance?

I referred to the Quora link here as well, but could not understand clearly. Can anyone please help me understand why and how variational inference underestimates the variance of the true posterior ...
• 161
1 vote
32 views

### Do these values of bias and variance make sense?

I have this code: ...
71 views

### How to define the features that bring more variance?

I have a dataset with 10 column, that are my features, and 1732 row that are my registrations. This registration are divided in 15 classes, so I have several registration for every class in my dataset....
• 481
1 vote
1k views

### How does bias and variance relate with the training/testing error in Machine Learning. In layman terms does high variance means high testing error

What causes high BIAS/VARIANCE and what are the consequences. Can someone explain in simple terms w.r.t to training/testing errors?
• 11
1 vote
318 views

### SKLearn PCA explained_variance_ration cumsum gives array of 1

I have a problem with PCA. I read that PCA needs clean numeric values. I started my analysis with a dataset called trainDf with shape ...
• 113
26 views

### Relationship of Bias and size of dataset

I was reading the following book: http://www.feat.engineering/resampling.html where the author mentioned the below: Generally speaking, as the amount of data in the analysis set shrinks, the ...
88 views

### Evaluating if metric of one group is higher than the metric of another group when group sizes differ significantly

I am working with a dataset that contains data of applicant income, gender, and loan status (whether or not the person was approved for a loan). I've created the following plots from the data. The ...
• 83
120 views

### learning curves of a classification algorithm

I a trying to understand this learning curve of a classification problem. But I am not sure what to infer. I believe that I have overfitting but I cannot sure. Very low training loss that’s very ...
• 121
1 vote
27 views

### Repeating values caught with a binary classifier

If my machine is broken, it starts to repeat certain channels. Thing is if there are no out-liars, it is difficult to tell it's broken as we would expect all data points to be around the same value. I ...
• 11
41 views

### I am not sure whether I am being asked to calculate the variance or the irreducible error

The question: Suppose we randomly sample a training set D from some unknown distribution. For each training set D we sample, we train a regression model to predict $y$ from $x$. We repeat this 10 ...
35 views

### Sampling a data based on average and variance of another data

I have a set of textual datasets that have the following average and variance tokens lengths: ...
• 262
1 vote
38 views

### Checking my understanding of the bias-variance tradeoff

Hello fellow DS aficionados, I'm trying to clarify some confusing feedback I got from a homework problem. We were told to recreate the bias-variance tradeoff graph using the first graph below as an ...
1 vote
73 views

### How can I calculate the level of agreement between my K-Means cluster labels and my ground truth labels in R?

I have made a K-Means clustering from 3 rasters with various values of k (k=2, k=4, k=7) and would like to know which values of k explains the most variance in my ground-truth data or the value of k ...
• 11
26 views

### Is it always that lower tree with higher bias but higher tree with higher varaince

When dealing with bias and variance trade-offs, I always hear that in tree models: shallow tree = high bias but low variance, deep tree = low bias but high variance. Someone may also quote from high ...
281 views

### Find variance from 2 variances of 2 datasets with difference sizes

In an attempt to find the mean number of hours his tutorial classmates spent per day preparing for tutorials, John collected data from 10 of his friends in the tutorial group and found that the mean ...
• 11
96 views

### 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 ...
105 views

### 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 ...
1 vote
25 views

### 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 ...
42 views

### 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: ...
79 views

### 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, ...
1 vote
67 views

### 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 ...
• 121
40 views

### 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 ...
290 views

1 vote
93 views

### Model Selection using Bias Variance Trade Off

I have a Regression Model with Train MAPE as 6% and Test MAPE as 15%. This appears to me as a clear case of over fitting. But can I still use this model assuming 15% Error is not a bad number after-...
• 11
1 vote
856 views

### PCA, covariance, eigenvector matrix and rotation [closed]

I am following the Coursera NLP specialization, and in particular the lab "Another explanation about PCA" in Course 1 Week 3. From the lab, I recovered the following code. It creates 2 ...
1 vote
35 views

### Is there a quantitative way to determine if a class of algorithms tends produce low bias or low variance models?

I understand that some machine learning models tend to be low bias, whereas others tend to be low variance (source). As an example, a linear regression will tend to have low variance error and high ...
• 13