Supervised Learning Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Mukudzidza kunotariswa, chii chinoita kuti dhata rekudzidzisa rive 'rakanyorwa'?
   • Muenzaniso wega wega wakarongedzerwa mumaforodha zvinoenderana nerudzi rwefaira uye saizi
   • Each example is paired with a target output for the model to learn
   • Muenzaniso wega wega wakamanikidzwa kuti modhi igone kuiverenga munguva shoma
   • Muenzaniso wega wega unosanganisira rekodha yenguva chaiyo payakatanga kuunganidzwa
2. Muenzaniso unofanotaura kana email iri spam kana kuti kwete spam iri kugadzirisa rudzi rwedambudziko rekudzidza rakatariswa?
   • Kudzokera shure
   • Kupatsanurwa
   • Dimensionality kuderedza
   • Clustering
3. Kufanotaura mutengo wekutengesa weimba kubva pakukura kwayo uye nzvimbo ndiyo muenzaniso wei?
   • Clustering, nekuti dzimba dzakafanana dzakaunganidzwa pamwe nenzvimbo
   • Regression, nekuti iyo inobuda inhamba inoenderera
   • Classification, nekuti dzimba ndedzenharaunda dzakasiyana
   • Kusimbisa kudzidza, nekuti mitengo mibayiro vatengesi
4. Sei varapi vachidzora imwe bvunzo seti iyo isingamboonekwe nemuenzaniso panguva yekudzidziswa?
   • Kupa iyo modhi mimwe mienzaniso kana kudzidziswa kwayo kwakwira
   • Chengetedza kopi yekuchengetedza kana data rekudzidzisa rarasika
   • Kufungidzira kuti iyo modhi inoita sei pane itsva, data isingaonekwe
   • Kuita kuti kudzidziswa kumhanye nekukurumidza nekushandisa mienzaniso mishoma
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