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

Explanation : 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

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.

To see how this is done, let W represent the event that the conversation was held with a woman, and L denote the event that the conversation was held with a long­haired person. It can be assumed that women constitute half the population for this example. So, not knowing anything else, the probability that W occurs is P(W) = 0.5. Suppose it is also known that 75% of women have long hair which we denote as P(L |W) = 0.75 (read: the probability of event L given event W is 0.75, meaning that the probability of a person having long hair (event "L"): given that we already know that the person is a woman ("event W") is 75%). Likewise, suppose it is known that 15% of men have long hair, or P(L |M) = 0.15; where M is the complementary event of W: i.e.; the event that the conversation was held with a man (assuming that every human is either a man or a woman).

Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, P(W |L). Using the formula for Bayes' theorem, we have:

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where we have used the law of total probability to expand

P(L),

The numeric answer can be obtained by substituting the above values into this formula (the algebraic multiplication is annotated using " *", the centered dot). This yields

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i.e., the probability that the conversation was held with a woman, given that the person had long hair is about 83%. More examples are provided below.

Another aspect of collaborative filtering systems is the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and help the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user

Explanation : Supervised learning asks the machine to learn from our data when we specify a target variable.

This reduces the machine's task to only divining some pattern from the input data to get the target variable.

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?

(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.

During the discovery phase you will be interviewing all the project stakeholders because they would be having quite a good amount of knowledge for the problem domain you will be working and you also interviewing project sponsors you will get to know what all are the expectations once project get completed. Hence, you will be noting down all the expectations from the project as well as you will be using their expertise in the domain.

The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.

The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.

Support vector count and cluster density do not apply to recommender systems.

MAE and AUC are both valid and useful metrics for measuring recommender systems.

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, text­like, 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.

This is the standard solution of the normal equations for linear regression. Because A is not square, you cannot simply take its inverse.

SVMs can be used to solve various real world problems:

• SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

• Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.

• SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.

• Hand-written characters can be recognized using SVM

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.

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.

Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.

The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.

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