6 votes
Accepted

Is there a connection between the bias term in a linear regression model and the bias that can lead to under-fitting?

In machine learning, the term bias can refer to at least 2 related concepts A (learnable) parameter of a model, such as a linear regression model, which allows you to learn a shifted function. For ...
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4 votes
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What's the difference between estimation and approximation error?

Section 5.2 Error Decomposition of the book Understanding Machine Learning: From Theory to Algorithms (2014) gives a description of the approximation error and estimation error in the context of ...
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3 votes
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Can I compute the fitness of an agent based on a low number of runs of the game?

You can probably get away with a relatively low X for two reasons: The Central Limit Theorem. This tells us that the accuracy in the estimate of an agent's fitness will improve as the square root of ...
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3 votes
Accepted

What is the bias-variance trade-off in reinforcement learning?

The bias-variance trade-off that you're referring to has to do with the return estimator. Any RL algorithm you choose needs some estimate of the cumulative return, which is a random variable with many ...
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  • 991
2 votes

How does Monte Carlo have high variance?

When using terms like "high" for high variance, this is in comparison to other methods, mainly in comparison to TD learning, which bootstraps between single time steps. It is worth spelling out what ...
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2 votes
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What makes a machine learning algorithm a low variance one or a high variance one?

What this is talking about is how much a machine learning algorithm is good at "memorizing" the data. Decision trees, for their nature, tend to overfit very easily, this is because they can separate ...
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  • 136
1 vote

Why does k-means have more bias than spectral clustering and GMM?

I'm not an expert on clustering, but here's my take below. Note that this is only based on theoretical arguments, I haven't had enough clustering experience to say if this is generally true in ...
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  • 1,260
1 vote
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How can I determine the bias and variance of a random forrest?

To gain a good understanding of this, I recommend first reading about the trade-off between bias and variance in ML and AI methods. A great article on this topic that I recommend as a light ...
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  • 211
1 vote

Why is having low variance important in offline policy evaluation of reinforcement learning?

Bias is not necessarily bad, even though the term bias usually has a negative connotation. In fact, in machine learning, inductive bias is quite important and necessary. For example, if you want to ...
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1 vote
Accepted

Why is having low variance important in offline policy evaluation of reinforcement learning?

Having low variance is important in general as it reduces the number of samples needed to obtain accurate estimates. This is the case for all statistical machine learning, not just reinforcement ...
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