Questions tagged [variance]
The variance tag has no usage guidance.
118
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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 ...
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Leave One Subject Out Cross Validation: mean vs median
Assume we have a dataset with n subjects and m labels and train a classifier. To ensure that there is no subject bias in the ...
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Why Seaborn's histplot takes a longer time to plot when the data is far from the median than when it is closer?
I have recently noticed that, for the some number of data points and for the same precision (int64), when using Seaborn's histplot on data that has huge variance with respect to the median it takes a ...
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Generate new sample with higher variance, covariance from empirical distribution
I am analyzing the joint distribution of three continuous variables. I would like to sample from the joint distribution of these three variables, which is straight forward. However, I also want to ...
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Understanding loss function/ implementing my own
I am currently working on an ETA prediction LightGBM model (regression tree) for which I want the negative residuals to be penalized higher than the positive. I understand that a custom loss function ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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....
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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:
...
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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 ...
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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 ...
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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 ...
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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 ...
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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
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..
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4
..
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2
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1
Let's say I want to improve the accuracy of Y1. I am ...
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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}...
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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 ...
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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 ...
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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 ...
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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 ...
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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:
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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-...
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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 ...
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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-...
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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 ...
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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 ...
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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?
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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 ...
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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 \...
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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 ...
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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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