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Questions tagged [bias]

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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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How to calculate Bias and Standard deviation given the true versus predicted values of ML model?

I am looking to calculate Bias and Standard deviation given the true versus predicted values of ML model. Am I correct in saying: Bias = (average difference between true values and predicted values)/...
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svm optimization problem

Suppose we have the dataset: {(3,1),(3-1),(6,1),(6,-1)} {(1,0),(0,1),(0,-1),(-1,0)} the first set represent the positive label, and de second the negative. I want manually find the support vectors, ...
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LSTM regression bias increases when targets go close to 0

I've build a LSTM model for time series forecasting. Results are not bad, with a mean normalized error of 7%. However, this normalized bias shows a clear pattern: The closer to 0 the value to predict, ...
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Survey results analysis for a social media app - stratified sampling?

I found this question on Glassdoor and was hoping to get some input on it... A user satisfaction survey was conducted for two groups for a social media platform. Assume large sample sizes. Group 1 ...
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1answer
35 views

Free parameters in logistic regression

When applying logistic regression, one is essentially applying the following function $1/(1 + e^{\beta x})$ to provide a decision boundary, where $\beta$ are a set of parameters that are learned by ...
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1answer
27 views

Training dataset decreasing in quality (Google data science blog)

I have a complex algorithm that decides when it should show customers of an only shop an ad on our website, after they log in, in hope that they will buy what is in the ad. We have no control what is ...
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Predicting relative performance of algorithm configurations on a task

Setup I have a set of tasks $T = t_0, \dots, t_k$, and an algorithm that can be configured with a set of settings $S = s_0, \dots, s_m$. Each task $t_i$ also has a set of properties $P_i = p_0, \dots,...
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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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0answers
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Bias of 1 in fully connected layers introduced dying relu problem

While implementing AlexNet (model-code), one of the thing I need to do was to initialize the biases of the convolutional layers and fully connected layers. Normally we initialize biases with 0s, but ...
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1answer
59 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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When should the bias b be updated with weights w and when should it be updated seperately?

It seems in some Machine Learning models, the bias term $b$ is updated just like other weights $w_i, i=1...n$. For example, in Logistic Regression, using SGD, $b \ \text{or} \ w_0$ is updated with: $$...
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How to understand the bias term b and how it is updated in ADALINE algorithm?

With $y=x*w+b$, where $x$ is the feature vector of a sample and $w$ is the weight vector, the update rule (SGD) for the bias $b$ is: $b \leftarrow b + \eta(o - y)$. With Gradient Descent, the final $...
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2answers
176 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 ...
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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.
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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
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Can You Purposely Bias A Clustering Model?

We have a large amount (Billions) of high cardinality, mixed nominal & numerical data, and are performing some clustering on it as an experiment. There is a small subset of these data, however, ...
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1answer
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Weights and bias' relative to preprocessed X

I am currently using sklearn scale to preprocess my X data before being put into a perceptron - mean/stddev so as to prevent the ...
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190 views

Correcting log-bias in the output of an XGB

I have previously worked with GAMs, where I was trying to do regression on a log-transformed variable. The log-transformation introduced a negative bias in the average of the predicted variable, and I ...
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Estimating bias when humans are bad

Andrew Ng recommends evaluating the bias of your machine learning algorithm by figuring out how the algorithm's error rate compares to human error rate, with the idea that humans are probably fairly ...