{"id":671,"date":"2022-06-02T05:09:05","date_gmt":"2022-06-02T05:09:05","guid":{"rendered":"https:\/\/emorah.com\/story\/?p=671"},"modified":"2022-08-08T05:11:50","modified_gmt":"2022-08-08T05:11:50","slug":"machine-learning-exploring-the-model-fresco-play-mcqs-answers","status":"publish","type":"post","link":"https:\/\/emorah.com\/story\/fresco-play\/machine-learning-exploring-the-model-fresco-play-mcqs-answers\/","title":{"rendered":"Machine Learning – Exploring the Model Fresco Play MCQs Answers"},"content":{"rendered":"\n

Machine Learning – Exploring the Model Fresco Play MCQs Answers<\/strong><\/p>\n\n\n\n

Disclaimer: The main motive to provide this solution is to help and support those who are unable to do these courses due to facing some issue and having a little bit lack of knowledge. All of the material and information contained on this website is for knowledge and education purposes only.<\/strong><\/p>\n\n\n\n

Try to understand these solutions and solve your Hands-On problems. (Not encourage copy and paste these solutions)<\/strong><\/p>\n\n\n\n

Quiz on Cost Function and Gradient Descent<\/strong><\/p>\n\n\n\n

1.What is the name of the function that takes the input and maps it to the output variable called?<\/p>\n\n\n\n

  1. Map Function<\/li>
  2. None of the options<\/li>
  3. Hypothesis Function<\/li>
  4. Model Function<\/li><\/ol>\n\n\n\n

    Answer: 3)Hypothesis Function<\/p>\n\n\n\n

    2.What is the process of dividing each feature by its range called?<\/p>\n\n\n\n

    1. Feature Scaling<\/li>
    2. None of the options<\/li>
    3. Feature Dividing<\/li>
    4. Range Dividing<\/li><\/ol>\n\n\n\n

      Answer: 1)Feature Scaling<\/p>\n\n\n\n

      3.Problems that predict real values outputs are called __________<\/p>\n\n\n\n

      1. Classification Problems<\/li>
      2. Regression Problems<\/li>
      3. Real Valued Problems<\/li>
      4. Greedy Problems<\/li><\/ol>\n\n\n\n

        Answer: 2)Regression Problems<\/p>\n\n\n\n

        4.The result of scaling is a variable in the range of [1 , 10].<\/p>\n\n\n\n

        1. False<\/li>
        2. True<\/li><\/ol>\n\n\n\n

          Answer: 1)False<\/p>\n\n\n\n

          5.The objective function for linear regression is also known as Cost Function.<\/p>\n\n\n\n

          1. False<\/li>
          2. True<\/li><\/ol>\n\n\n\n

            Answer: 2)True<\/p>\n\n\n\n

            6.What is the Learning Technique in which the right answer is given for each example in the data called?<\/p>\n\n\n\n

            1. Unsupervised Learning<\/li>
            2. Supervised Learning<\/li>
            3. Reinforcement Learning<\/li>
            4. Right Answer Learning<\/li><\/ol>\n\n\n\n

              Answer: 2)Supervised Learning<\/p>\n\n\n\n

              7.Output variables are also known as feature variables.<\/p>\n\n\n\n

              1. False<\/li>
              2. True<\/li><\/ol>\n\n\n\n

                Answer: 1)False<\/p>\n\n\n\n

                8.Input variables are also known as feature variables.<\/p>\n\n\n\n

                1. False<\/li>
                2. True<\/li><\/ol>\n\n\n\n

                  Answer: 2)True<\/p>\n\n\n\n

                  9.____________ controls the magnitude of a step taken during Gradient Descent.<\/p>\n\n\n\n

                  1. Parameter<\/li>
                  2. Step Rate<\/li>
                  3. Momentum<\/li>
                  4. Learning Rate<\/li><\/ol>\n\n\n\n

                    Answer: 4)Learning Rate<\/p>\n\n\n\n

                    10.Cost function in linear regression is also called squared error function.<\/p>\n\n\n\n

                    1. False<\/li>
                    2. True<\/li><\/ol>\n\n\n\n

                      Answer: 2)True<\/p>\n\n\n\n

                      11.For different parameters of the hypothesis function, we get the same hypothesis function.<\/p>\n\n\n\n

                      1. False<\/li>
                      2. True<\/li><\/ol>\n\n\n\n

                        Answer: 1)False<\/p>\n\n\n\n

                        12.How are the parameters updated during Gradient Descent process?<\/p>\n\n\n\n

                        1. Sequentially<\/li>
                        2. Simultaneously<\/li>
                        3. Not updated<\/li>
                        4. One at a time<\/li><\/ol>\n\n\n\n

                          Answer: 2)Simultaneously<\/p>\n\n\n\n

                          Quiz on Gradient Descent<\/strong><\/p>\n\n\n\n

                          1.For ____________, the error is determined by getting the proportion of values misclassified by the model.<\/p>\n\n\n\n

                          1. Classification<\/li>
                          2. Clustering<\/li>
                          3. None of the options<\/li>
                          4. Regression<\/li><\/ol>\n\n\n\n

                            Answer: 1)Classification<\/p>\n\n\n\n

                            2.High values of threshold are good for the classification problem.<\/p>\n\n\n\n

                            1. True<\/li>
                            2. False<\/li><\/ol>\n\n\n\n

                              Answer: 2)False<\/p>\n\n\n\n

                              3.Underfit data has a high variance.<\/p>\n\n\n\n

                              1. True<\/li>
                              2. False<\/li><\/ol>\n\n\n\n

                                Answer: 2)False<\/p>\n\n\n\n

                                4.____________ function is used as a mapping function for classification problems.<\/p>\n\n\n\n

                                1. Linear<\/li>
                                2. Sigmoid<\/li>
                                3. Convex<\/li>
                                4. Concave<\/li><\/ol>\n\n\n\n

                                  Answer: 2)Sigmoid<\/p>\n\n\n\n

                                  5.Classification problems with just two classes are called Binary classification problems.<\/p>\n\n\n\n

                                  1. True<\/li>
                                  2. False<\/li><\/ol>\n\n\n\n

                                    Answer: 1)True<\/p>\n\n\n\n

                                    6.Where does the sigmoid function asymptote?<\/p>\n\n\n\n

                                    1. -1 and +1<\/li>
                                    2. 0 and 1<\/li>
                                    3. -inf and +inf<\/li>
                                    4. 0 and inf<\/li><\/ol>\n\n\n\n

                                      Answer: 2)0 and 1<\/p>\n\n\n\n

                                      7.Lower Decision boundary leads to False Positives during classification.<\/p>\n\n\n\n

                                      1. False<\/li>
                                      2. True<\/li><\/ol>\n\n\n\n

                                        Answer: 2)True<\/p>\n\n\n\n

                                        8.Linear Regression is an optimal function that can be used for classification problems.<\/p>\n\n\n\n

                                        1. False<\/li>
                                        2. True<\/li><\/ol>\n\n\n\n

                                          Answer: 1)False<\/p>\n\n\n\n

                                          9.For ____________, the error is calculated by finding the sum of squared distance between actual and predicted values.<\/p>\n\n\n\n

                                          1. Regression<\/li>
                                          2. None of the options<\/li>
                                          3. Classification<\/li>
                                          4. Clustering<\/li><\/ol>\n\n\n\n

                                            Answer: 1)Regression<\/p>\n\n\n\n

                                            10.I have a scenario where my hypothesis fits my training set well but fails to generalize for the test set. What is this scenario called?<\/p>\n\n\n\n

                                            1. Underfitting<\/li>
                                            2. Generalization Failure<\/li>
                                            3. Overfitting<\/li>
                                            4. None of the options<\/li><\/ol>\n\n\n\n

                                              Answer: 3)Overfitting<\/p>\n\n\n\n

                                              11.What is the range of the output values for a sigmoid function?<\/p>\n\n\n\n

                                              1. [0,.5]<\/li>
                                              2. [-inf,+ inf]<\/li>
                                              3. [0,1]<\/li>
                                              4. [0,inf]<\/li><\/ol>\n\n\n\n

                                                Answer: 3)[0,1]<\/p>\n\n\n\n

                                                12.____________ is the line that separates y = 0 and y = 1 in a logistic function.<\/p>\n\n\n\n

                                                1. Divider<\/li>
                                                2. None of the options<\/li>
                                                3. Separator<\/li>
                                                4. Decision Boundary<\/li><\/ol>\n\n\n\n

                                                  Answer: 4)Decision Boundary<\/p>\n\n\n\n

                                                  13.Reducing the number of features can reduce overfitting.<\/p>\n\n\n\n

                                                  1. False<\/li>
                                                  2. True<\/li><\/ol>\n\n\n\n

                                                    Answer: 2)True<\/p>\n\n\n\n

                                                    14.A suggested approach for evaluating the hypothesis is to split the data into training and test set.<\/p>\n\n\n\n

                                                    1. True<\/li>
                                                    2. False<\/li><\/ol>\n\n\n\n

                                                      Answer: 1)True<\/p>\n\n\n\n

                                                      15.Overfitting and Underfitting are applicable only to linear regression problems.<\/p>\n\n\n\n

                                                      1. True<\/li>
                                                      2. False<\/li><\/ol>\n\n\n\n

                                                        Answer: 2)False<\/p>\n\n\n\n

                                                        16.Overfit data has high bias.<\/p>\n\n\n\n

                                                        1. False<\/li>
                                                        2. True<\/li><\/ol>\n\n\n\n

                                                          Answer: 1)False<\/p>\n\n\n\n

                                                          ML Exploring the Model – Final Quiz<\/strong><\/p>\n\n\n\n

                                                          1.For an underfit data set, the training and the cross-validation error will be high.<\/p>\n\n\n\n

                                                          1. True<\/li>
                                                          2. False<\/li><\/ol>\n\n\n\n

                                                            Answer: 1)True<\/p>\n\n\n\n

                                                            2.For an overfit data set, the cross-validation error will be much bigger than the training error.<\/p>\n\n\n\n

                                                            1. True<\/li>
                                                            2. False<\/li><\/ol>\n\n\n\n

                                                              Answer: 1)True<\/p>\n\n\n\n

                                                              3.Problems, where discrete-valued outputs are predicted, are called?<\/p>\n\n\n\n

                                                              1. Real Valued Problems<\/li>
                                                              2. Classification Problems<\/li>
                                                              3. Greedy Problems<\/li>
                                                              4. Regression Problems<\/li><\/ol>\n\n\n\n

                                                                Answer: 2)Classification Problems<\/p>\n\n\n\n

                                                                4.What measures the extent to which the predictions change between various realizations of the model?<\/p>\n\n\n\n

                                                                1. Deviation<\/li>
                                                                2. Bias<\/li>
                                                                3. Variance<\/li>
                                                                4. Difference<\/li><\/ol>\n\n\n\n

                                                                  Answer: 3)Variance<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                  Machine Learning – Exploring the Model Fresco Play MCQs Answers Disclaimer: The main motive to provide this solution is to help and support those who are unable to do these courses due to facing some issue and having a little bit lack of knowledge. All of the material and information contained […]<\/p>\n","protected":false},"author":3,"featured_media":663,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":""},"categories":[156],"tags":[],"_links":{"self":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/671"}],"collection":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/comments?post=671"}],"version-history":[{"count":2,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/671\/revisions"}],"predecessor-version":[{"id":674,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/posts\/671\/revisions\/674"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/media\/663"}],"wp:attachment":[{"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/media?parent=671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/categories?post=671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/emorah.com\/story\/wp-json\/wp\/v2\/tags?post=671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}