Databricks-Certified-Professional-Data-Scientist Databricks Certified Professional Data Scientist Exam Questions and Answers

Explanation : You can use the linear regression model for predicting the continuous

output variable based on the input variables. In all the cases mentioned in the

question option, you can see that output

can be predicted based on the input variable.

Option-A: Input: Location, House Area and Output: House Price

Option-B : Input: Weather condition, Output: Demand for the goods and services

Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction

Option-D : Input: Number of students and Output: Sale quantity of text book

A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represents classification rules.

In decision analysis a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.

A decision tree consists of 3 types of nodes:

1. Decision nodes - commonly represented by squares

2. Chance nodes - represented by circles

3. End nodes - represented by triangles

Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities.

Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods.

Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

Regression algorithms are usually employed when the data points are inherently numerical variables (such as the dimensions of an object the weight of a person, or the temperature in the atmosphere) but unlike Bayesian algorithms, they're not very good for categorical data (such as employee status or credit score description).

Principal component analysis (PCA) involves a mathematical procedure that

transforms a number of (possibly) correlated variables into a

(smaller) number of uncorrected variables called principal components. The first

principal component accounts for as

much of the variability in the data as possible, and each succeeding component

accounts for as much of the remaining variability as possible.

R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.

In unsupervised learning we don't have a target variable as we did in

classification and regression.

Instead of telling the machine Predict Y for our data X, we're asking What can you

tell me about X?

Things we ask the machine to tell us about

X may be What are the six best groups we can make out of X? or What three

features occur together most frequently in X?

Depending on the size of the data you are uploading, Amazon S3 offers the following options:

Logistic regression

Pros: Computationally inexpensive, easy to implement knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values^ nominal values

To answer whether the model you are building is robust enough or not you need to

have answer below questions at least

- Model is performing as expected with the test data or not?

- Whatever hypothesis defined in the initial phase is being tested or not?

- Do we need more data?

- Domain experts are convinced or not with the model?

And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.

The entire problem can be solved using below method

Binomial: A manufacturer of metal pistons finds that on the average, 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?

Poisson: A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies

Poisson: Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.

Normal: It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20 03 mm and 20.08 mm

Explanation :

The difference between their properties can be promptly summarized as follows:

Table Description automatically generated

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component

accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.

The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected

onto any other line, including the yellow one.