Questions tagged [variance]
The variance tag has no usage guidance.
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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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6 views
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 ...
0
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0answers
9 views
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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0answers
17 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 ...
0
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1answer
39 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 ...
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0answers
8 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 ...
0
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1answer
23 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: ...
0
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1answer
10 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
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1answer
28 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 ...
2
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1answer
21 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
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X100
Y1
....
Y10
1
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4
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1
Let's say I want to improve the accuracy of Y1. I am ...
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58 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}...
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1answer
24 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 ...
0
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1answer
19 views
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 ...
0
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0answers
6 views
Calculate Variance from Ward distances
The Ward method for clustering minimizes the total within-cluster variance.
So I suppose that there is a link between the Ward distances that I got with the linkage function, and the variance of the ...
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2answers
114 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 ...
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0answers
15 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:
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0answers
85 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-...
4
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2answers
233 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 ...
1
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1answer
41 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-...
1
vote
1answer
92 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
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1answer
17 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 ...
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1answer
258 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?
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0answers
15 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 ...
1
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1answer
24 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 \...
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0answers
18 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 ...
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0answers
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....
0
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1answer
45 views
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 ...
2
votes
1answer
243 views
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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3answers
1k views
What are bias and variance in machine learning?
I am studying machine learning, and I have encountered the concept of bias and variance. I am a university student and in the slides of my professor, the bias is defined as:
$bias = E[error_s(h)]-...
1
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1answer
24 views
Can the extent of variability within a dataset be reflected through clustering?
As an example: I need to compare the extent of variability amongst houses belonging to 4 different architectural eras - I want to see how different the houses are within each group and then compare ...
2
votes
1answer
63 views
Which between random forest or extra tree is best in a unbalance dataset?
I have an unbalanced dataset, with 3 classes, with 60% of class 1, 38% of class 2, and 2% of class 3.
I don't want to generate more examples of class 3, and I cannot get more examples of class 3.
The ...
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0answers
114 views
What is the difference between domain randomization and data augmentation?
Domain randomization (https://arxiv.org/abs/1703.06907) is used to create a synthetic dataset with enough variance that it will encompass unseen real data, as just one variation.
I am trying to ...
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2answers
1k views
Variance as criteria for feature selection
I'm working on an unsupervised clustering problem. I read multiple times that a variable with higher variance can be chosen over a variable with a lower variance. For example, scikit-learn implements ...
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0answers
570 views
How to calculate irreducible error using Bias and Variance for a given machine learning Model?
I am trying to calculate the Bias and Variance for a ML Model.
$$
Err(x)=E[(Y−\hat f(x))^2]
\\Err(x)=Bias^2+Variance+Irreducible\ Error
$$
$\hat f(x)$ is our model
$Y$ is the variable we are trying ...
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0answers
14 views
Coefficient of variance problem
I am doing a project and I am facing a problem I have no idea how to solve.
I have a data series of stock prices, and, based on this series I obtained another series, of so-called rentabilities (which ...
3
votes
1answer
123 views
Bagging vs pasting in ensemble learning
This is a citation from "Hands-on machine learning with Scikit-Learn, Keras and TensorFlow" by Aurelien Geron:
"Bootstrapping introduces a bit more diversity in the subsets that each predictor is ...
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1answer
57 views
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1answer
101 views
Computing variance of an SGD iteration
It is known that SGD iteration has huge variance.
Given the iteration update:
$$
w^{k+1} := w^k - \underbrace{\alpha \ g_i(w^k)}_{p^k},
$$
where $w$ are model weights and $g_i(w^k)$ is gradient of ...
1
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1answer
20 views
How to find average lag time with variance & confidence of two time series
I have two variables as time series, one a consequent of the other, I would like to find the average time delay it takes the dependent variable to act on the independent variable. Additionally, I ...
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1answer
2k views
plotting histogram together with variance
I have a small piece of code that print values gotten from a csv file into a histogram. This is all done using matplotlib library
...
2
votes
1answer
3k views
matplotlib graph to plot values and variance
I am really new to the world of matplotlib graphing as well as using those graphs to understand data.
I have written a simple python code where I read a .csv file ...
2
votes
2answers
118 views
Importance of Variance in Machine Learning
Selecting a column from a Dataframe, plotting its Histogram using matplotlib and then finding the variance is the steps that I have to take for this part of a project.
The final goal of the project ...
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1answer
110 views
Does variance and standard deviation both measure how spread out the numbers are?
I've heard from different sources that, standard deviation measures how spread out the numbers are. But I've also heard the same for variance.
Is it technically correct to say this statement for both ...
3
votes
1answer
38 views
When unsupervised learning is more beneficial in comparison with supervised learning even the labelings are existed?
When unsupervised learning is more beneficial in comparison with supervised learning even the labeling are existed?
If there is no labeling the unsupervised learning is better than supervised learning ...
2
votes
1answer
13 views
Technique to determine variation in metric due to varying parameters
So basically I have a large set of features corresponding to a metric - like many ML problems.
What I want to know is: can we correlate the variance of metric with the variation in each feature.
ex:
...
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0answers
11 views
Comparison of dispersion measures of different samples with different subsamples
Context: I have data on movie ratings and I want to find what factors affect the rating the most. I have $k$ features $F_1, F_2,\ldots,F_k$ characterizing a movie - for example, director, lead actor, ...
0
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1answer
26 views
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 ...
1
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2answers
417 views
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 ...
5
votes
2answers
687 views
Bagging vs Boosting, Bias vs Variance, Depth of trees
I understand the main principle of bagging and boosting for classification and regression trees. My doubts are about the optimization of the hyperparameters, especially the depth of the trees
First ...
2
votes
1answer
90 views
Co-joining multi-peak histograms
I am analysing a bunch of data files which represent responsiveness of cells to addition of a drug. If a drug is not added, cell responds normally, if it is added, it shows abnormal patterns:
, .
We ...
