Questions tagged [variance]

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9
votes
1answer
505 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 ...
7
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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 ...
6
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3answers
4k views

Overfitting Naive Bayes

My question is what are potential reasons for Naive Bayes to perform well on a train set but poorly on a test set? I am working with a variation of the 20news dataset. The dataset has documents, ...
5
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2answers
95 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.
5
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2answers
1k views

How do you set sigma for the Gaussian similarity kernel?

Let's say we have $n$ two-dimensional vectors: $$\mathbf{x}_1,\dots,\mathbf{x}_i,\dots,\mathbf{x}_n=(x_{1_1},x_{1_2})^T,\dots,(x_{i_1},x_{i_2})^T,\dots,(x_{n_1},x_{n_2})^T$$ How do you set $\sigma$ ...
4
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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 ...
3
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1answer
59 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 ...
3
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1answer
158 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 ...
3
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1answer
39 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 ...
3
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0answers
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 ...
2
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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: ...
2
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1answer
75 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?
2
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2answers
72 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 ...
2
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1answer
59 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/...
2
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0answers
23 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 ...
2
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0answers
96 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 ...
1
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2answers
92 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 ...
1
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2answers
127 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 ...
1
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2answers
120 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. ...
1
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3answers
953 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 ...
1
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2answers
181 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 ...
1
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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 ...
1
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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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0answers
10 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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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 ...
1
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1answer
26 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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0answers
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 ...
1
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1answer
66 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
18 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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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 ...
1
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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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0answers
412 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 ...
1
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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 ...
0
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4answers
159 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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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, ...
0
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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 $x_1, x_2, ..., x_n$...
0
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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 ...
0
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1answer
484 views

How to measure variance in a classification dataset?

I have a dataset that contains 20 predictor variables (both categorical and numeric) and one target variable (2 classes - Good and Bad). But, there are only 23 observations in the dataset. While I ...
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0answers
4 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'...
0
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1answer
39 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?
0
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0answers
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 ...
0
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1answer
60 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 ...
0
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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 ...
0
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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 ...
0
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1answer
73 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-...
0
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0answers
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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0answers
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 ...
0
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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)...
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 ...