Please consider improvement for page - 1

[burlachenkok]

1. In the CLI, you can add a requirement for a variance limit and math. the expectation is bounded. The Cauchy distribution is sometimes encountered in engineering in the laser industry.

2. Bias - an error with the choice of a set of functions from which you choose, the objective function is far from the projection on this set of functions that you are considering.

3. Variance - associated with the instability of the solutions obtained based on sampling examples from the population.
   For (2) and (3) better read the Statistical Learning book, the definitions from CheatSheet are too vague at least for me.

4. In non-parametric models, these are models where there are no training parameters. For example, the nearest neighbor method. (Or more sophisticated models from MetaLearning that use in one of it's flavor non-parametric blocks)

5. Cross-Validation - does not do any validation, rather it is an assessment. If you want validation (in the sense of guarantees) - this principle is not suitable.

6. In Subset selection you put 0-norm, that's very bad jargon. Zero is not the norm. It is not homogeneous (multiplying a vector by 10 will not multiply a value by 10). Use the word cardinality(.)