## GUJARAT TECHNOLOGICAL UNIVERSITY DIPLOMA IN ENGINEERING - SEMESTER 4 - EXAMINATION - SUMMER 2026

Scikit-learn માં train\_test\_split() િવશે સમ7વો.

| Subject Code: DI04000061                                                                                                             | Subject Code: DI04000061                                                                                                             | Subject Code: DI04000061                                                                                                                                                                          | Date: 21/05/2026   |
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| Subject Name: Introduction Machine Learning                                                                                          | Subject Name: Introduction Machine Learning                                                                                          | Subject Name: Introduction Machine Learning                                                                                                                                                       |                    |
| Time: 10:30 AM TO 01:00 PM                                                                                                           | Time: 10:30 AM TO 01:00 PM                                                                                                           | Time: 10:30 AM TO 01:00 PM                                                                                                                                                                        | Total Marks: 70    |
| Instructions :                                                                                                                       | Instructions :                                                                                                                       | Instructions :                                                                                                                                                                                    |                    |
| 1. Attempt all questions.                                                                                                            | 1. Attempt all questions.                                                                                                            | 1. Attempt all questions.                                                                                                                                                                         |                    |
| 2. MakeSuitable assumptions wherever necessary                                                                                       | 2. MakeSuitable assumptions wherever necessary                                                                                       | 2. MakeSuitable assumptions wherever necessary                                                                                                                                                    |                    |
| 3. Figures to the right indicate full marks. 4. Use of simple calculators and non-programmable scientific calculators are permitted. | 3. Figures to the right indicate full marks. 4. Use of simple calculators and non-programmable scientific calculators are permitted. | 3. Figures to the right indicate full marks. 4. Use of simple calculators and non-programmable scientific calculators are permitted.                                                              |                    |
| 5. English version is authentic.                                                                                                     | 5. English version is authentic.                                                                                                     | 5. English version is authentic.                                                                                                                                                                  |                    |
| Q.1                                                                                                                                  | (a)                                                                                                                                  | Differentiate between Human Learning and Machine Learning.                                                                                                                                        | 03                 |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૧                                                | ( અ )                                                                                                                                | Human Learning અને મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ વGLYPH<c=23,font=/QOBAAA+ShrutiRegular>ચેનો તફાવત GLYPH<c=29,font=/QOBAAA+ShrutiRegular>પGLYPH<c=31,font=/QOBAAA+ShrutiRegular>ટ કરો. | ૦૩                 |
|                                                                                                                                      | (b)                                                                                                                                  | Describe the benefits and limitations of Machine Learning                                                                                                                                         | 04                 |
|                                                                                                                                      | ( બ )                                                                                                                                | મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ ના ફાયદા અને મયા&દાઓ( ું  વણ&ન કરો.                                                                                                                          | ૦૪                 |
|                                                                                                                                      | (c)                                                                                                                                  | Explain Applications of Machine Learning                                                                                                                                                          | 07                 |
|                                                                                                                                      | ( ક )                                                                                                                                | મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ ના ઉપયોગો (Applications) સમ7વો.                                                                                                                         | ૦૭                 |
|                                                                                                                                      |                                                                                                                                      | અથવા                                                                                                                                                                                               |                    |
| OR                                                                                                                                   | OR                                                                                                                                   | OR                                                                                                                                                                                                |                    |
|                                                                                                                                      | (c)                                                                                                                                  | List Tools and Technology for Machine Learning and explain about any one Tool.                                                                                                                    | 07                 |
|                                                                                                                                      | ( ક )                                                                                                                                | મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ  માટ8ના સાધનો (Tools) અને ટ8કનોલો;ની યાદ< આપો અને કોઈપણ                                                                                                         | ૦૭                 |
| એક સાધન િવશે સમ7વો.                                                                                                                      | એક સાધન િવશે સમ7વો.                                                                                                                      | એક સાધન િવશે સમ7વો.                                                                                                                                                                                   |                    |
| Q.2                                                                                                                                  | (a)                                                                                                                                  | Explain Bar Plot with suitable example.                                                                                                                                                           | 03                 |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૨                                                | ( અ )                                                                                                                                | યો@ય ઉદાહરણ સાથે બાર Dલોટ (Bar Plot) સમ7વો.                                                                                                                                                              | ૦૩                 |
|                                                                                                                                      | (b)                                                                                                                                  | List and explain any three statistical functions available in NumPy                                                                                                                               | 04                 |
|                                                                                                                                      | ( બ )                                                                                                                                | NumPy માં ઉપલIધ કોઈપણ Jણ Kકડાક<ય િવધેયો (Statistical Functions) ની યાદ< આપો અને સમ7વો.                                                                                                                        | ૦૪                 |
|                                                                                                                                      | (c)                                                                                                                                  | Explain Maths Functions: add(), subtract(), divide(), power(), sqrt().                                                                                                                            | 07                 |
|                                                                                                                                      | ( ક )                                                                                                                                | Maths Functions સમ7વો: add(), subtract(), divide(), power(), sqrt()                                                                                                                                | ૦૭                 |
| અથવા                                                                                                                                  | અથવા                                                                                                                                  | અથવા                                                                                                                                                                                               |                    |
| OR                                                                                                                                   | OR                                                                                                                                   | OR                                                                                                                                                                                                |                    |
| Q.2                                                                                                                                  | (a)                                                                                                                                  | Explain Histogram Plot with suitable example                                                                                                                                                      | 03                 |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૨                                                | ( અ )                                                                                                                                | યો@ય ઉદાહરણ સાથે XહGLYPH<c=29,font=/QOBAAA+ShrutiRegular>ટોYામ Dલોટ (Histogram Plot) સમ7વો.                                                                                                               | ૦૩                 |
|                                                                                                                                      | (b)                                                                                                                                  | Explain about train_test_split() in Scikit-learn.                                                                                                                                                 | 04                 |
|                                                                                                                                      | ( બ )                                                                                                                                |                                                                                                                                                                                                   | ૦૪                 |

|                                                                                       | (c)   | Explain Manipulating Functions: isnull(), sum(), min(), max() drop().                          | 07   |
|---------------------------------------------------------------------------------------|-------|------------------------------------------------------------------------------------------------|------|
|                                                                                       | ( ક ) | ડ8ટા મેનીD]ુલેXટ^ગ ફં_શ`સ સમ7વો: isnull(), sum(), min(), max(), drop().                               | ૦૭   |
| Q.3                                                                                   | (a)   | Explain about confusion matrix.                                                                | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૩ | ( અ ) | ક`ફbુઝન મેXd_સ િવશે સમ7વો                                                                           | ૦૩   |
|                                                                                       | (b)   | Explain K-fold cross-validation.                                                               | 04   |
|                                                                                       | ( બ ) | K-fold gોસ-વેhલડ8શન સમ7વો.                                                                        | ૦૪   |
|                                                                                       | (c)   | Elaborate Data quality and remediation technique.                                              | 07   |
|                                                                                       | ( ક ) | ડ8ટા _વોhલટ< અને તેને iુધારવાની તકનીકો િવશે િવગતવાર સમ7વો.                                                     | ૦૭   |
|                                                                                       |       | અથવા OR                                                                                         |      |
| Q.3                                                                                   | (a)   | Differentiate between numerical and categorical data.                                          | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૩ | ( અ ) | `]ુમેXરકલ અને ક8ટ8ગર<કલ ડ8ટા વGLYPH<c=23,font=/QOBAAA+ShrutiRegular>ચેનો તફાવત સમ7વો.                  | ૦૩   |
|                                                                                       | (b)   | Explain methods to improve the performance of a Machine Learning model.                        | 04   |
|                                                                                       | ( બ ) | મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ  મોડ8લની કામગીર< (Performance) i ુધારવા માટ8ની પjિતઓ સમ7વો. | ૦૪   |
|                                                                                       | (c)   | Explain following:                                                                             | 07   |
|                                                                                       |       | 1.  Dimensionality reduction 2.  Feature subset selection                                      | ૦૭   |
|                                                                                       | ( ક ) | નીચેના પદો સમ7વો: (Explain following:) 1.  ડાયમે`શનાhલટ< Xરડ_શન                                        |      |
|                                                                                       |       | 2.  ફ<ચર સબસેટ િસલે_શન                                                                            |      |
| Q.4                                                                                   | (a)   | List advantages and disadvantages of supervised learning.                                      | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૪ | ( અ ) | iુપરવાઇoડ લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગના ફાયદા અને ગેરફાયદાની યાદ< આપો.                       | ૦૩   |
|                                                                                       | (b)   | Explain simple linear regression with real-world examples.                                     | 04   |
|                                                                                       | ( બ ) | ઉદાહરણો સાથે િસpપલ લીિનયર XરYેશન સમ7વો.                                                                 | ૦૪   |
|                                                                                       | (c)   | Explain working of k-NN algorithm with diagram.                                                | 07   |
|                                                                                       | ( ક ) | Diagram સાથે k-NN અrગોXરધમની કાય&પjિત સમ7વો.                                                          | ૦૭   |
|                                                                                       |       | અથવા OR                                                                                         |      |
| Q.4                                                                                   | (a)   | Explain Learning Steps in Supervised Machine Learning                                          | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૪ | ( અ ) | iુપરવાઇoડ મશીન લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગ Learning Steps સમ7વો                      | ૦૩   |
|                                                                                       | (b)   | Explain types of linear regression with equation.                                              | 04   |
|                                                                                       | ( બ ) | લીિનયર XરYેશન ના sકારો સમીકરણ સાથે સમ7વો.                                                                | ૦૪   |
|                                                                                       | (c)   | Explain Support Vector Machine (SVM) fundamentals.                                             | 07   |

|                                                                                       | ( ક )   | સપોટ& વે_ટર મશીનના પાયાના િસjાંતો (Fundamentals) સમ7વો.                                | ૦૭   |
|---------------------------------------------------------------------------------------|---------|----------------------------------------------------------------------------|------|
| Q.5                                                                                   | (a)     | List Real-world examples of unsupervised Learning.                         | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૫ | ( અ )   | અનiુપરવાઇoડ લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગના ઉદાહરણોની યાદ< આપો         | ૦૩   |
|                                                                                       | (b)     | Define Generative AI And List applications of Generative AI.               | 04   |
|                                                                                       | ( બ )   | જનર8Xટવ AI ની vયાwયા આપો અને તેના ઉપયોગો જણાવો.                                       | ૦૪   |
|                                                                                       | (c)     | Explain clustering technique with suitable example.                        | 07   |
|                                                                                       | ( ક )   | યો@ય ઉદાહરણ સાથે _લGLYPH<c=29,font=/QOBAAA+ShrutiRegular>ટXર^ગ તકનીક સમ7વો.      | ૦૭   |
|                                                                                       | અથવા     | OR                                                                         |      |
| Q.5                                                                                   | (a)     | Explain Working of Generative AI.                                          | 03   |
| GLYPH<c=1,font=/QZAAAA+BalooBhai2Regular> GLYPH<c=2,font=/QZAAAA+BalooBhai2Regular> ૫ | ( અ )   | જનર8Xટવ AI ની કાય&પjિત સમ7વો.                                                  | ૦૩   |
|                                                                                       | (b)     | Explain working of unsupervised learning with examples.                    | 04   |
|                                                                                       | ( બ )   | ઉદાહરણો સાથે અનiુપરવાઇoડ લિનGLYPH<c=20,font=/QOBAAA+ShrutiRegular>ગની કાય&પjિત સમ7વો. | ૦૪   |
|                                                                                       | (c)     | Explain Association rules technique with suitable example.                 | 07   |
|                                                                                       | ( ક )   |                                                                            | ૦૭   |

યો@ય ઉદાહરણ સાથે એસોિસએશન xrસ તકનીક સમ7વો.

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