Databricks-Certified-Professional-Data-Scientist 무료 덤프문제 온라인 액세스

Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,

• A.P(A,B|C) P(B|C) =P(B|A,C)
• B.P(A,B|C) P(B|C) =P(C|B,C)
• C.P(A,B|C) P(B|C) =P(A|C,B)
• D.P(A,B|C) P(B|C) =P(A|B,C)

In which of the following scenario we can use naTve Bayes theorem for classification

• A.To identify whether a fruit is an orange or not based on features like diameter, color and shape
• B.To classify whether an email is spam or not spam
• C.Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

Refer to Exhibit

In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?

• A.Logistic Regression
• B.Linear Regression
• C.Association Rules
• D.Discriminant Analysis

You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?

• A.Include the number of times each of the words appears in the book in your model
• B.Include least mutual information with other selected features as a feature selection criterion
• C.Decrease the size of our training data
• D.Evaluate a model that only includes the top 100 words

While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

• A.1
• B.n/2
• C.0
• D.2