Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam braindumps download (DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST exam dumps Free)

QUESTION: 41

Which of the following is a correct example of the target variable in regression (supervised learning)?

Nominal values like true, false
Reptile, fish, mammal, amphibian, plant, fungi
Infinite number of numeric values, such as 0.100, 42.001, 1000.743..
All of the above

Answer(s): D

Explanation:

We address two cases of the target variable. The first case occurs when the target variable can take only nominal values: true or false; reptile, fish: mammal, amphibian, plant, fungi. The second case of classification occurs when the target variable can take an infinite number of numeric values, such as 0.100, 42.001, 1000.743, .... This case is called regression.

Show Answer Next Question

QUESTION: 42

Select the correct algorithm of unsupervised algorithm

K-Nearest Neighbors
K-Means
Support Vector Machines
Naive Bayes

Answer(s): A

Explanation:

Sup Supervised learning tasks
Classification Regression k-Nearest Neighbors Linear
Naive Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso

Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window

Show Answer Next Question

QUESTION: 43

Classification and regression are examples of___________.

supervised learning
un-supervised learning
Clustering
Density estimation

Answer(s): A

Explanation:

In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points. Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.

Show Answer Next Question

QUESTION: 44

Reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions. It is done in_______

supervised learning
un-supervised learning
k-Nearest Neighbors
Support vector machines

Answer(s): B

Explanation:

The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions

Show Answer Next Question

QUESTION: 45

If you are trying to predict or forecast a discrete target value, then which is the correct options

Supervised Learning regression algorithms
Supervised Learning classification algorithms
Un supervised Learning
Density estimation algorithm

Answer(s): B

Explanation:

If you're trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No: 1/2/3, A/B/C: or Red/Yellow/Black? If so:
then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00: or-999 to 999, or +_to -_, then you need to look into regression.

Show Answer Next Question

Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps Databricks Certified Professional Data Scientist Exam Questions 41–45 (page: 10)

QUESTION: 41

Explanation:

QUESTION: 42

Explanation:

QUESTION: 43

Explanation:

QUESTION: 44

Explanation:

QUESTION: 45

Explanation:

Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 41–45 (page: 10)