machine learning with python ibm coursera final exam answers
Final Exam
1. Which of the following is an example of Machine Learning?
- Streaming service viewing suggestions.
- Websites recommending items to purchase.
- Telecommunication companies predicting subscriber retention.
- All of the above.
2. Which of the following is a Machine Learning technique?
- Clustering
- Classification
- Regression/Estimation
- All of the above
3. Which of the following is true for Multiple Linear Regression?
- Observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.
- One independent variable is used to predict a dependent variable.
- Multiple independent variables are used to predict a dependent variable.
- The relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x.
4. Which of the below is an example of a classification problem?
- To predict the category to which a customer belongs to.
- To predict whether a customer switches to another provider/brand.
- To predict whether a customer responds to a particular advertising campaign or not.
- All of the above.
5. Which of the following statements are TRUEabout Logistic Regression? (select two)
- Logistic regression finds a regression line through the data to predict the probability of a point belonging to a class.
- Logistic regression is analogous to linear regression but takes a categorical/discrete target field instead of a numeric one.
- Logistic regression can be used both for binary classification and multi-class classification.
- In logistic regression, the dependent variable is always binary.
6. Which statement is FALSEabout k-means clustering?
- k-means divides the data into non-overlapping clusters without any cluster-internal structure.
- The objective of k-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.
- As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.
7. Which of the following statements is false for k-means clustering?
- k-means clustering creates a tree of clusters
- The object of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.
- k-means divides the data into non-overlapping clusters without any cluster-interval structure.
- None of the above
8. What are some advantages of logistic regression over SVM?
- It focuses on finding the best margin to separate classes in one iteration.
- It can be used for linearly separable data.
- It works well with high-dimensional data, such as text or image.
- It focuses on attaining the right probability for each output class.
9. In comparison to mean absolute error, mean squared error:
- Is more interpretable by taking the same unit as the response.
- Avoids cancellation of errors.
- Focuses more on large errors.
- Weighs small and large errors equally.
10. Which of the following is more suitable to solve with a decision tree?
- To segment customers into groups with similar characteristics.
- To predict the salary of a baseball player based on the number of home runs and years in the league.
- To predict if the person will like a certain movie based on age, favorite actors and genre.
- To predict the probability of raining based on current temperature and humidity.
