Correct Answer: ABCD

Explanation: When your analytical project successfully completed they come up with the following at the end of the projects. Presentations- You will be having presentations like for the all the stakeholders, generally these presentation will

help seniors executives to make better decisions. Similarly you would be creating presentations for the other teams like analysts various visuals you would be creating like ROC Curves, Heat Maps, and Bar Charts etc.

Whatever tools you have used like SAS, R, or Python then accordingly code was developed and you will get that code as one of the outcome. Also you would have created a technical specifications for implementing the codes.

Correct Answer: AB

Explanation: SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is

done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text-like, or word-like data.

This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector

location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.

An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

Correct Answer: C

Explanation: Error calculation allows you to see how well a machine learning method is performing.

One way of determining this performance is to calculate a numerical error This number is sometimes a percent,

however it can also be a score or distance. The goal is usually to minimize an error percent or distance:

however th goal may be to minimize or maximize a score. Encog supports the following error calculation methods.

Sum of Squares Error (ESS)

Root Mean Square Error (RMS)

Mean Square Error (MSE) (default)

SOM Error (Euclidean Distance Error)

RMSE measures error of a predicted numeric value, and so applies to contexts like regression and some recommender system techniques, which rely on predicting a numeric value. It is not relevant to classification techniques

like logistic regression and Naive Bayes, which predict categorical values. It also is not relevant to unsupervied techniques like clustering. The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used

measure of the

differences between values predicted by a model or an estimator and the values actually observed. Basically,

the RMSD represents the sample standard deviation of the differences between predicted values and observed values.

These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the

magnitudes

of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy,

but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.

Correct Answer: D

Explanation: (Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.

Correct Answer: DE

Explanation: This question wants to check your understanding about Bl and Data Science. Bl was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in Bl or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc. But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.

Correct Answer: A

Explanation: Feature Selection:

The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate

analysis evaluates. Them according to the magnitude of the components of the discriminate vector.

Correct Answer: A

Correct Answer: ABD

Explanation: : k-Nearest Neighbors Pros: High accuracy insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values

Correct Answer: C

Explanation: Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.

Correct Answer: A

: A data model explicitly describes a relationship between predictor and response variables. Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models. Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation. If you need to fit data with a nonlinear model, transform the variables to make the relationship linear. Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.