All Questions
Tagged with bias bias-variance-tradeoff
52
questions
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28
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Why does increasing model complexity reduce bias over the entire data distribution?
In ML, we often talk about the bias-variance tradeoff, and how increasing model complexity both reduces bias and increases variance. I understand why increasing model complexity reduces bias at first, ...
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Does the intuitive sense of overfitting in this mechanism design context exemplify bias-variance tradeoff?
Suppose the (we can say unanimous) preference of each individual in a society is to select roads for travel by placing 95% weight on the objective of minimizing travel time, and the remaining 5% ...
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1
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147
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How to avoid bias/avoid overfitting when choosing a machine learning model? [closed]
My typical workflow in the past, when creating machine learning models, has been to do the following:
Decide on some candidate model families for the task at hand.
Divide dataset into train and test ...
anon
2
votes
1
answer
371
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Philosophical insight of Bias Variance Decomposition
As we know that we can perform a Bias Variance decomposition of an Estimator with MSE as loss function and it will look like below:
$$\operatorname{MSE}(\hat{\theta}) = \operatorname{tr}(\operatorname{...
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3
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3
answers
2k
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If we reduce size of training dataset does it decreases bias?
I'm a newbie and learning ML. I've a doubt, normally we know we should increase the size of training dataset or should add more data to reduce variance (fairly understood why). Now variance has ...
- 133
2
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1
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79
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Cross-validation: error estimation and bias
When obtaining the error estimation of a model over a dataset using k-fold cross-validation, lower values of the error estimation necessarily imply a lower bias? Are both concepts, error estimation ...
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1
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0
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495
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When do control variables increase precision?
Suppose we're interested in the effect $\beta$ of a treatment $D$. To increase the precision of our estimate (ie., reduce the variance of $\hat{\beta}$), we can include a control variable $X$ that ...
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1
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1
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179
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Reasons to prefer low bias with higher variance over the alternative (and vice versa)
I am trying to understand the bias-variance tradeoff in practice. I have read several related questions and answers, but still have a few questions:
Assume we are estimating a structural equation ...
- 115
6
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2
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Does bias eventually increase with model complexity?
Does bias eventually increase with model complexity?
Reasoning behind the question:
If I understand it correctly, "bias" measures the discrepancy between the expected value of our model's ($...
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3
votes
0
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415
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Apart from the Bias-Variance "Decomposition" - is there a Bias-Variance "Proof"?
I am sure at some point, many of us have come across the "Bias-Variance Tradeoff" : The "error" of any "estimator" (e.g an estimator can be considered as a linear ...
2
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1
answer
135
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Bias-variance trade-off in case of biased estimators: is the bias zero?
Consider a data generating process (DGP) that is AR(1): $y_t=\varphi_1 y_{t-1}+\varepsilon_t$ with $\varepsilon_t\sim i.i.D(0,\sigma^2)$ for some distribution $D$ with mean zero and variance $\sigma^2$...
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why test error and variance has different curve in bias variance trade off graph?
In bias variance trade off graph
Bias is the difference between actual and predicted value in training data set
so train error (dotted red curve) and bias(red curve ) looks same
Variance is the ...
- 135
1
vote
2
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1k
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Definition of the bias of an estimator
I'm quite confused about the definition of the bias of an estimator.
Suppose we have unknown distribution $P(x, \theta)$, and construct the
estimator $\hat{\theta}$ that maps the observed data sample ...
- 11
2
votes
1
answer
88
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Bias of MLE scales with $1/N$?
I was reading this paper (link) and it gave me some confusion.
$P(r|\theta)$ is a distribution that generates sample $r$ based on some Poisson distribution, whose mean and variance are defined as some ...
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13
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4
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What is meant by Low Bias and High Variance of the Model?
I am new in this field of Machine Learning. From what I get by the definition,
Bias: It simply represents how far your model parameters are from true parameters of the underlying population.
$$ Bias(\...
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