You can overfit anything with just one parameter

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In summary, overfitting refers to a situation where a model fits the training data too closely, resulting in poor performance on new, unseen data. It can occur with any type of data and can be prevented through techniques such as regularization, cross-validation, and early stopping. While there may be cases where overfitting is acceptable, in most cases it should be avoided to ensure the generalizability and reliability of the model. The consequences of overfitting a model can include poor performance on new data, difficulty interpreting the results, and increased likelihood of errors and biases.
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BWV
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Interesting paper here:

https://colala.bcs.rochester.edu/papers/piantadosi2018one.pdf

which using a closed-form solution to the logistic map m(z) = 4z(1−z) of
mk (θ) = sin2 [ 2k arcsin √ θ]

with a finely tuned parameter θ (extending to hundreds of decimal places) can fit just about any 2 dimensional shape such as:

Elephant.png
 

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Related to You can overfit anything with just one parameter

1. What does it mean to "overfit" something with just one parameter?

Overfitting is a term used in machine learning and statistical modeling to describe a situation where a model fits the training data too closely, resulting in poor performance on new, unseen data. In the context of having just one parameter, it means that the model is overly simplistic and may not accurately capture the underlying patterns and relationships in the data.

2. Can overfitting occur with any type of data?

Yes, overfitting can occur with any type of data, including numerical, categorical, and text data. It is a common problem in data analysis and machine learning, and it is important to be aware of it and take steps to prevent it.

3. How can overfitting be prevented?

There are several techniques that can be used to prevent overfitting, including regularization, cross-validation, and early stopping. These techniques involve adjusting the parameters of the model or using different training/validation data to find the optimal balance between model complexity and performance.

4. Is overfitting always a bad thing?

While overfitting is generally undesirable, there are some cases where it may be acceptable or even desirable. For example, in some applications, such as anomaly detection, having a highly overfit model may be necessary to accurately identify rare events in the data. However, in most cases, overfitting should be avoided in order to ensure the generalizability and reliability of the model.

5. What are the consequences of overfitting a model?

The consequences of overfitting a model can include poor performance on new data, difficulty interpreting the results, and increased likelihood of errors and biases. In addition, an overfit model may not be able to accurately capture the underlying relationships in the data, making it less useful and reliable for making predictions or drawing conclusions.

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