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Questions tagged [variance]

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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?
mon's user avatar
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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 ...
Shreedatta Nasik's user avatar
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1 answer
49 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 ...
mon's user avatar
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1 answer
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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 ...
Mario's user avatar
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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 ...
Krailon's user avatar
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1 answer
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Question about bias-variance trade off

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 ...
pixel.t87's user avatar
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1 answer
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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 ...
Daniel Jiang's user avatar
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1 answer
157 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. ...
Mahesha999's user avatar
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1 answer
44 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 ...
asaddummie's user avatar
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1 answer
53 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 ...
Kodak's user avatar
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3 answers
155 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 ...
ali's user avatar
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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 ...
Peter's user avatar
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1 answer
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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 ...
Rohan's user avatar
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0 answers
338 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 ...
Curious's user avatar
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31 views

Do these values of bias and variance make sense?

I have this code: ...
kasofi9051's user avatar
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1 answer
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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....
Inuraghe's user avatar
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1 answer
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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?
honolulu's user avatar
1 vote
1 answer
300 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 ...
Kalizi's user avatar
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1 answer
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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 ...
Aditya Singh Rathore's user avatar
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2 answers
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 ...
Erik M's user avatar
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2 votes
1 answer
106 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 ...
xavi's user avatar
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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 ...
gotenks's user avatar
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1 answer
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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 ...
rachel tan's user avatar
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1 answer
34 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: ...
Minions's user avatar
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0 answers
36 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 ...
FLAN - Legacy's user avatar
1 vote
0 answers
72 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 ...
R.runya's user avatar
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0 answers
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 ...
rain keyu's user avatar
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0 answers
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 ...
Lucifer's user avatar
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0 answers
94 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 ...
airpoll_epi's user avatar
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1 answer
104 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 ...
DataVader's user avatar
1 vote
0 answers
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 ...
todapod264's user avatar
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1 answer
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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: ...
Chukwudi Ogbonna's user avatar
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1 answer
75 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, ...
Mourad Askar's user avatar
1 vote
1 answer
65 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 ...
Eran Moshe's user avatar
2 votes
1 answer
39 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 ...
Sad CRUD Developer's user avatar
3 votes
0 answers
278 views

Unbiasedness of random forests

Suppose that I am trying to build a random forest by subsampling the data and choosing a single feature per tree randomly. For example, suppose there is some dataset, $D = \{(x_{1},y_{1}), ......(x_{N}...
user1234's user avatar
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1 vote
1 answer
62 views

Understanding the computation of Standard Error of Regression Coeficients (ISLR)

During my reading of Introduction To Statistical Learning, I reached this part and I'm facing a very hard time trying to understand what is being said: The highlighted words are telling that this is ...
Paulo's user avatar
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0 votes
1 answer
1k views

derivation for expected value for variance

Hi Im taking a course about probability distribution in datascience and below is derivation of the expected value for the variance Variance = expected value of the squared difference from mean for ...
star's user avatar
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1 answer
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mean and variance of a dataset

I have a simple question. Please see the below screenshot : It is from a midterm exam from a university : https://cedar.buffalo.edu/~srihari/CSE555/exams/midterm-solution-2006.pdf My questions is how ...
John adams's user avatar
0 votes
2 answers
141 views

Figure out relative importance of entity attributes

I'm trying to understand how various aspects of a movie contribute to its gross revenue. I want to rank a movie's attributes in that sense - the attributes that most strongly determine the revenue are ...
user9343456's user avatar
1 vote
0 answers
26 views

KNN with high-variance data [closed]

KNN doesn't work well with high-variance data, so how should I fit my data? Here is an example of what the data looks like:
Bruno Salles's user avatar
1 vote
0 answers
116 views

Levene test for equal variance

I would like to run one-way ANOVA test on my data. I saw that one of several assumptions for one-way ANOVA is that there needs to be homogeneity of variances. I have run the test for different data-...
Reut's user avatar
  • 349
5 votes
2 answers
1k views

Elimination of features based on high covariance without affecting performance?

I ran into a question where the answer ran me into a big doubt. Suppose we have a dataset $A=${$x1,x2,y$} in which $x1$ and $x2$ are our features and $y$ is the label. Also, suppose that the ...
user307393's user avatar
1 vote
2 answers
87 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-...
Manish's user avatar
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1 vote
1 answer
766 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 ...
robertspierre's user avatar
1 vote
1 answer
34 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 ...
Brian_E's user avatar
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0 votes
1 answer
2k views

How can I compute the ideal variance threshold value for my data?

I have a dataset that contains n features scaled between [0,1]. I would use an unsupervised feature selection algorithm (variance thresholding). How can I compute the threshold value?
Ramzi's user avatar
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1 vote
0 answers
18 views

Confidence interval for class membership probabilities

I would like to calculate confidence intervals for predicted probabilities of a class membership obtained with randomForest. I know I could use predict.all in randomForest(), which gives me the ...
user2888990's user avatar
1 vote
1 answer
138 views

Find inputs that give highest variance in output space

I have a relatively simple problem that I think should have a known solution, but that I can't figure out. Any help would be greatly appreciated. Basically, I have a function $f : \mathbb{R}^d \...
Alan Wang's user avatar
0 votes
0 answers
22 views

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 ...
Feng Chen's user avatar
  • 207