22 questions linked to/from What problem do shrinkage methods solve?
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### Intuitive explanation of the bias-variance tradeoff?

I am looking for an intuitive explanation of the bias-variance tradeoff, both in general and specifically in the context of linear regression.
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### Why must one trade off between bias and variance?

Apparently, a learning algorithm must make a trade off between bias and variance when producing a hypothesis. Bias means systematic deviation from data. Variance refers to the error due to ...
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### Error increase on L2 regularization in an NN

When introducing L2 regularization on my neural network, there is a point during training where the error starts to increase after having reached a value very close to 0. This is due to the fact that ...
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### Omitted variable bias in linear regression

I have a philosophical question regarding omitted variable bias. We have the typical regression model (population model) $$Y= \beta_0 + \beta_1X_1 + ... + \beta_nX_n + \upsilon,$$ where the ...
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### When will a less true model predict better than a truer model?

In "To Explain or to Predict?", Pr. Galit Shmueli said that sometimes a less true model can predict better than a truer model. Why is it so? When will it happen? How does it happen? Is ...
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### Which ML Algorithms are affected by dummy variable trap?

My understanding is that regression models are affected by the dummy variable trap. What about other machine learning algorithms e.g. linear svm, logistic regression? Also, if an algorithm is not ...
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### How to interprete lasso from lars correctly?

I tried the lars package with R and got the following result. ...
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### Do stepwise regression techniques increase a model's predictive power?

I understand some of the many problems of stepwise regression. However, as an academic endeavor, assume I want to use stepwise regression for a predictive model, and I want to better understand the ...
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