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Scikit-learn Test
Test duration:
20
min
No. of questions:
10
Level of experience:
Expert

Scikit-learn Test

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Scikit-learn Online Test

Scikit-learn is a free software machine learning library mainly used for the Python programming language. It also helps to provide a range of supervised and unsupervised learning algorithms via a consistent interface in Python. Scikit-learn library is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.


Scikit-learn online test helps tech recruiters and hiring managers to assess candidates' machine learning skills with Scikit-learn. Scikit-learn skills test is designed by experienced subject matter experts (SMEs) to evaluate and hire machine learning engineers as per industry standards.


Scikit-learn online test helps to screen the candidates who possess traits as follows:

 

  • Good knowledge of Preprocessing, Classification, and Clustering in Scikit-learn
  • Experience in data cleaning and data manipulation processes
  • Understanding of Model Selection, Decision Tree, and Hyper-Parameter Tuning
  • Familiarity with terms like pipelining, encoding, and imputations
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Test Summary

Scikit-learn skills test is a secure and reliable way of candidate assessment. You can use our role-based access control feature to restrict system access based on the roles of individual users within the recruiting team. Features like window violation, image, audio, and video proctoring help detect cheating during the test.

This test may contain MCQs (Multiple Choice Questions), MAQs (Multiple Answer Questions), Fill in the Blanks, Whiteboard Questions, Audio / Video Questions, LogicBox (AI-based Pseudo-Coding Platform), Coding Simulators, True or False Questions, etc.

Useful for hiring
  • Machine Learning Engineer
  • Data Scientist
  • Python Developer
Test Duration
20
min
No. of Questions
10
Level of Expertise
Expert
Topics Covered
Shuffle

Preprocessing

Classification

Shuffle

Clustering Dimensionality

Shuffle

Reduction

Shuffle

Pipelining

Shuffle

Encoding

Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.
Question type
Multiple Option
Topics covered
Preprocessing
Difficulty
Hard

Question:

On a given data, on using the standard linear-based models, the result obtained is not good enough for our needs. Therefore, you conclude that the model should have some non-linear features which have been missed. How would you add the complexity of non-linear features to your model?


Options

  • sklearn.preprocessing.Features()
  • sklearn.preprocessing.PolynomialFeatures()
  • sklearn.preprocessing.Normalize()
  • None of the options
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questions to the test

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