Questions tagged [variance]
The variance tag has no usage guidance.
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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 ...
4
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2answers
63 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 ...
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11 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 ...
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2answers
81 views
Determining if a time series is random
An example time series would be the stock market, which is sometimes described as a random walk. Over time, this is clearly not the case as it has essentially gone in one direction (up) with only ...
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5 views
Derive main prediction for zero-one loss function
Intuitively I can see why mode of predictions is the main prediction of a zero-one loss function, but mathematically I am not sure how it is derived?
Main prediction $= argmin_{y'}E_D[L(\widehat y, y'...
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36 views
Learning curve using micro F-score and macro F-score
I plotted the learning curves using micro and macro F-scores for a Multinomial Naive Bayes classifier.
The first plot is made using micro F-score, and the second using macro F-score. I find it quite ...
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0answers
14 views
Sampling trying to keep as much multivariate variance as possible
We can use PCA for dimensionality reduction, but at the cost of getting "uninterpretable" variables.
I was thinking if anyone considered a sampling technique that would try to aim keeping as much of ...
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1answer
40 views
What variance threshold to consider in feature selection?
Consider a numerical dataset with continuous variables, that has been scaled to end up with values in the [0,1] range. How can I compute a reasonable variance threshold for all the variables?
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12 views
Evaluating randomness in a model performance
I'm evaluating the variability in performance (AUC) in the test set of a machine learning model with an intrinsic random component (xgboost). How many sources of variation should I use?
Just ...
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1answer
60 views
What does high variance mean in a binary classification machine learning model?
My understanding of high variance is that the targets are spread widely around. The output values are "all over the place".
In a binary classification model, there can only be 2 outcomes. I am at a ...
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2answers
93 views
Why is there a trade-off between bias and variance in supervised learning? Why can't we have best of both worlds?
The bias-variance trade-off is like a law in machine learning. You cannot have the best of both worlds. What is it about supervised learning in machine learning that makes it impossible to satisfy the ...
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1answer
27 views
Measure degree of heteroscedasticity [closed]
I analyzed my time series using Breusch Pagan test and observed the presence of heteroscedasticity in it. After box-cox transformation, I again tested the time series using Breusch Pagan test. The ...
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15 views
Identify appropriate variance stabilization transform
I have a heavy tailed data which is heteroscedastic in nature. I need to apply some ML algorithms on this data that are good for normal distribution. So I want to transform the data. I tried Box cox ...
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1answer
60 views
Variance in statistics vs machine learning
In basic statistics, variance is a measure the variability of the data about its mean. In machine learning, variance is a measure of learning the training data too well/capturing the noise in the data/...
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1answer
65 views
Implication of a dominant Principal Component in PCA analysis
I need help, are there any practical implications of a dominant principal component. For example, if of three PCs, PC1 explains almost 100% of the variance in this dataset, What does this mean in ...
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1answer
28 views
Bias and variance in the model o in the predictions?
This topic confuses me. In the literature or articles, when talking about bias and variance in automatic learning, specifically in cross-validation, do they refer to the high bias (underfitting) and ...
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1answer
68 views
bias variance decomposition for classification problem
It is given that:
MSE = bias$^2$ + variance
I can see the mathematical relationship between MSE, bias, and variance. However, how do we understand the mathematical intuition of bias and variance for ...
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0answers
52 views
Math behind, MSE = bias^2 + variance
Based on the deeplearningbook:
$$MSE = E[(\theta_m^{-} - \theta)^2]$$
$$equals$$
$$Bias(\theta_m^{-})^2 + Var(\theta_m^{-})$$
where m is the number of samples ...
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0answers
19 views
ZCA: Covariance of Large Image Database
I have an overall question if my method is sound, so please bear with me in this description :).
I have a large image database and I wanted to create a preprocessing step using ZCA. The issue is that ...
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19 views
F-test for comparing the mean of two groups
What would be the procedure, in terms of F-test, if I would like to check if the mean of one group is greater than the other group (alternative hypothesis: $\mu_1 > \mu_2$)?
And also what would ...
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1answer
75 views
Massive variation in results with tensorflow and keras
I'm new to Tensorflow and Keras and I some background knowledge of how CNN's work.
I'm using a basic sequential model based on the code by https://pythonprogramming.net/convolutional-neural-network-...
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1answer
76 views
Bias-variance tradeoff and the uncertainty principle
Bias variance tradeoff seems to behave like the uncertainty principle, is it just another name for the same principle?
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7 views
Two tailed F test, correct rejection of the null hypothesis
I am performing an F test for equality of two variances by following this website formula https://itl.nist.gov/div898/handbook/eda/section3/eda359.htm, however it states that
In the above formulas ...
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4 views
What are some common sources of variance in a convolutional neural network DICE scores?
I know it's basically impossible to pinpoint the exact answer to this question without data, code, architecture, etc, but I'm curious as to if anyone has any general ideas on why this might be ...
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2answers
136 views
Why can decision trees have a high amount of variance
I've heard that decision trees can have a high amount of variance, and that for a data set $D$ split into test/train the decision tree could be quite different depending on how the data was split. ...
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3answers
1k views
Why underfitting is called high bias and overfitting is called high variance?
I have been using terms like underfitting/overfitting and bias-variance tradeoff for quite some while in data science discussions and I understand that underfitting is associated with high bias and ...
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0answers
24 views
Measure of variety within list/cluster
I have a dataset of about 53000 points. It has been clustered twice, based on two sets of unrelated attributes. For the first clustering (clustering 1) I used DBScan, and it ended up with about 700 ...
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0answers
64 views
Finding unaffected data in a dataset
The data set has missing values. Further examination tells that they are spread along 1.5 standard deviation from the median with distribution mean = 0 & variance = 5. How much data (in percentage)...
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0answers
11 views
Would it make technical sense to model its two known subelements separately (CLV example)?
Let's assume Customer Lifetime Value (CLV) is defined as average basket x frequency.
Option A is to build model predicting CLV ...
3
votes
1answer
162 views
Bias-variance tradeoff in practice (CNN)
I first trained a CNN on my dataset and got a loss plot that looks somewhat like this:
Orange is training loss, blue is dev loss. As you can see, the training loss is lower than the dev loss, so I ...
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vote
2answers
196 views
Linear machine learning algorithms “often” have high bias/low variance?
In this blog, which explains the meaning of bias and variance in machine learning, there's a line under the heading "Bias-Variance Trade-Off"
which says:
Parametric or linear machine learning ...
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4answers
170 views
Meaning of variance in machine learning models
I know that high variance cause overfitting, and high variance is that the model is sensitive to outliers.
But can I say Variance is that when the predicted points are too prolonged lead to high ...
0
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1answer
46 views
Initialisation methods and variance
I'm trying to understand some weights initialisation methods by reading the article http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf .
But I don't understand their notation on variance. Right ...
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2answers
1k views
Relationship between train and test error
I have some specific questions for which I could not extract answers from books. Therefore, I ask for help here and shall be extremely grateful for an intuitive explanation if possible.
In general, ...
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1answer
3k views
RL Advantage function why A = Q-V instead of A=V-Q?
In RL Course by David Silver - Lecture 7: Policy Gradient Methods, David explains what an Advantage function is, and how it's the difference between Q(s,a) and the V(s)
Preliminary, from this post:
...
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3answers
3k views
What is the meaning of term Variance in Machine Learning Model?
I am familiar with terms high bias and high variance and their effect on the model.
Basically your model has high variance when it is too complex and sensitive too even outliers.
But recently I was ...
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0answers
98 views
Evaluation of regression models with different evaluations (MSE, variance, VAF etc.)
When comparing several regression models in terms of quality, it seems like most have agreed on the MSE.
There are also papers comparing "variance" and "variance accounted for (VAF)".
However, there ...
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1answer
69 views
Maths question on mean squared error being dervied to bias and variance
I am reading a book and have difficulty in understanding the math on bias-
variance tradeoff. Below is the section that I am having trouble with:
Given a set of training samples $x_1, x_2, ..., x_n$...
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1answer
26 views
sort occurrence matrix to minimize its spatial variance
Is there any algorithm or an approach that sort occurrence matrix to reduce its spatial variance. I mean by spatial variance moving window variance (n*n moving box).
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2answers
211 views
How is the equation for the relation between prediction error, bias, and variance defined?
I'm reading this article Understanding the BiasVariance Tradeoff. It mentioned:
If we denote the variable we are trying to predict as $Y$ and our
covariates as $X$, we may assume that there is a ...
0
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1answer
21 views
How is the property in eq 15 obtained for Xavier initialization
I am new in this field so please be gentle with terminology.
In the original paper; "Understanding the difficulty of training deep feedforward neural networks", I dont understand how equation 15 is ...
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3answers
1k views
How to estimate the variance of regressors in scikit-learn?
Every classifier in scikit-learn has a method predict_proba(x) that predicts class probabilities for x. How to do the same thing ...
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0answers
416 views
How to sort tensor along two axis by variance in TensorFlow?
I have a function that visualizes 2D convolutional kernels. The weights come as 4D-tensors [filter_height, filter_width, in_channels, out_channels], as specified for conv2d. Now I try to sort the ...
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1answer
3k views
How to decide what threshold to use for removing low-variance features?
How to decide what threshold to use for removing low-variance features?
Particularly, I have 100000 features and the variances look like:
Could I e.g. take the average and use it to split this to ~...
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2answers
96 views
Trade off between Bias and Variance [closed]
What are the best ideas or approaches to trade off between bias and variance in Machine Learning models.
9
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1answer
511 views
Question on bias-variance tradeoff and means of optimization
So I was wondering how does one, for example, can best optimize the model they are trying to build when confronted with issues presented by high bias or high variance. Now, of course, you can play ...
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1answer
55 views
very low variance explained after applying pca
I applied PCA on MNIST data and found that the first 64 components are able to retain 86% of variance.
Is there any problem while applying pca to a big dataset like MNIST. Because in most of the ...
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1answer
74 views
Convert a pdf into a conditional pdf such that mean increases and std dev falls
Let success metric(for some business use case I am working on) be a continuous random variable S.
The mean of pdf defined on S indicates the chance of success. Higher the mean more is the chance of ...
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1answer
40 views
Bootstrapping or Randomly Dividing Dataset to reduce variance?
If I have 10,000 training samples then what should I do:
Bootstrapping and train 10 classifiers on it and then aggregating
Or
randomly divide the dataset into 10 parts and train 10 classifiers on ...
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2answers
131 views
How can I calculate mean and variance incrementally?
Say I have a set S of values, and want to store in a database some summary information about that set, so that later when I acquire a new value v I can make a reasonable estimate of what the summary ...
