पर्यवेक्षित शिक्षण प्रश्नोत्तरी

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

1. पर्यवेक्षित शिक्षण में, प्रशिक्षण डेटासेट को 'लेबल' क्यों बनाया जाता है?
   • Each example has been sorted into folders according to its file type and size
   • Each example is paired with a target output for the model to learn
   • Each example has been compressed so the model can read it in less time
   • Each example includes a timestamp recording exactly when it was first collected
2. एक मॉडल जो भविष्यवाणी करता है कि कोई ईमेल स्पैम है या नहीं, वह किस प्रकार की पर्यवेक्षित शिक्षण समस्या का समाधान कर रहा है?
   • प्रतिगमन
   • वर्गीकरण
   • आयामीता में कमी
   • क्लस्टरिंग
3. किसी घर के आकार और स्थान के आधार पर उसके बिक्री मूल्य की भविष्यवाणी करना किसका उदाहरण है?
   • Clustering, because similar houses are grouped together by area
   • Regression, because the output is a continuous number
   • Classification, because houses belong to distinct neighborhoods
   • Reinforcement learning, because prices reward sellers
4. चिकित्सक एक अलग परीक्षण सेट को क्यों रोकते हैं जिसे मॉडल प्रशिक्षण के दौरान कभी नहीं देखता है?
   • To give the model extra examples once its training accuracy peaks
   • To store a backup copy in case the training data is lost
   • To estimate how well the model performs on new, unseen data
   • To make training run faster by using fewer examples
5. When should preprocessing steps such as fitted scaling be learned for a supervised-learning evaluation?
   • From all rows before splitting so the test set helps estimate them
   • From test examples only
   • From the training partition, then applied to validation and test examples
   • From whichever partition gives the best final score
6. The guide’s classifier catches 30 of 40 urgent messages and flags 20 ordinary messages. Which recall and precision values follow?
   • Recall 75%; precision 60%
   • Recall 60%; precision 75%
   • Recall 100%; precision 50%
   • Recall 30%; precision 20%
7. A dataset has repeated records for the same person. What risk can a random row split create?
   • The model is guaranteed to have low recall
   • Related records can appear in both training and test sets, leaking information
   • The target labels automatically become numerical
   • The test set is no longer allowed to contain labels
8. What can changing a classification threshold do, according to the guide?
   • Change the tradeoff between false positives and false negatives without changing learned parameters
   • Change the training labels into features
   • Guarantee both error types become zero
   • Replace the need to evaluate model performance