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Two-chair problem #2

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Two-chair problem #2

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6 changes: 6 additions & 0 deletions README.md
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0. [Есть два стула](#0-Есть-два-стула)
1. [Why do you use feature selection?](#1-why-do-you-use-feature-selection)
2. [Explain what regularization is and why it is useful](#2-explain-what-regularization-is-and-why-it-is-useful)
3. [What’s the difference between L1 and L2 regularization?](#3-whats-the-difference-between-l1-and-l2-regularization)
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15. [Describe the differences between and use cases for box plots and histograms](https://github.com/iamtodor/data-science-interview-questions-and-answers#15-describe-the-differences-between-and-use-cases-for-box-plots-and-histograms)
16. [How would you find an anomaly in a distribution?](https://github.com/iamtodor/data-science-interview-questions-and-answers#16-how-would-you-find-an-anomaly-in-a-distribution)

## 0. Есть два стула
Есть два стула, на одном пики точены, на другом х;; д;;;ны, на какой сядешь, на какой мать посадишь?

Ответ: Возьму пики точены, срублю хуи дрочены, сам сяду и мать посажу.

## 1. Why do you use feature selection?
Feature selection is the process of selecting a subset of relevant features for use in model construction. Feature selection is itself useful, but it mostly acts as a filter, muting out features that aren’t useful in addition to your existing features.
Feature selection methods aid you in your mission to create an accurate predictive model. They help you by choosing features that will give you as good or better accuracy whilst requiring less data.
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