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Guide for Databricks Certified Professional Data Scientist

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Quiz

1/10
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,
Select the answer
1 correct answer
A.
P(A,B|C) P(B|C) =P(A|B,C)
B.
P(A,B|C) P(B|C) =P(B|A,C)
C.
P(A,B|C) P(B|C) =P(C|B,C)
D.
P(A,B|C) P(B|C) =P(A|C,B)

Quiz

2/10
Feature Hashing approach is "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" now with large vectors or with multiple locations per feature in Feature hashing?
Select the answer
1 correct answer
A.
Is a problem with accuracy
B.
It is hard to understand what classifier is doing
C.
It is easy to understand what classifier is doing
D.
Is a problem with accuracy as well as hard to understand what classifier us doing FEATURE HASHING 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.

Quiz

3/10
What are the advantages of the Hashing Features?
Select the answer
2 correct answers
A.
Requires the less memory
B.
Less pass through the training data
C.
Easily reverse engineer vectors to determine which original feature mapped to a vector location 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.

Quiz

4/10
Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?
Select the answer
1 correct answer
A.
It creates the smaller models
B.
It requires the lesser memory to store the coefficients for the model
C.
It reduces the non-significant features e.g. punctuations
D.
Noisy features are removed 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.

Quiz

5/10
What is the considerable difference between L1 and L2 regularization?
Select the answer
1 correct answer
A.
L1 regularization has more accuracy of the resulting model
B.
Size of the model can be much smaller in L1 regularization than that produced by L2-regularization
C.
L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
D.
All of the above are correct The two most common regularization methods are called L1 and L2 regularization. L1 regularization penalizes the weight vector for its L1-norm (i.e. the sum of the absolute values of the weights), whereas L2 regularization uses its L2-norm. There is usually not a considerable difference between the two methods in terms of the accuracy of the resulting model (Gao et al 2007), but L1 regularization has a significant advantage in practice. Because many of the weights of the features become zero as a result of L1-regularized training, the size of the model can be much smaller than that produced by L2-regularization. Compact models require less space on memory and storage, and enable the application to start up quickly. These merits can be of vital importance when the application is deployed in resource-tight environments such as cell-phones. Regularization works by adding the penalty associated with the coefficient values to the error of the hypothesis. This way, an accurate hypothesis with unlikely coefficients would be penalized whila a somewhat less accurate but more conservative hypothesis with low coefficients would not be penalized as much. 81

Quiz

6/10
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...
Select the answer
1 correct answer
A.
L2 is the sum of the square of the weights, while L1 is just the sum of the weights
B.
L1 is the sum of the square of the weights, while L2 is just the sum of the weights
C.
L1 gives Non-sparse output while L2 gives sparse outputs
D.
None of the above Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares: Certification Exam Databricks Certified Professional Data Scientist Databricks Databricks-Databricks-Certified-Professional-Data-Scientist 2-291726220

Quiz

7/10
Select the correct option which applies to L2 regularization
Select the answer
3 correct answers
A.
Computational efficient due to having analytical solutions
B.
Non-sparse outputs
C.
No feature selection Explanation : The difference between their properties can be promptly summarized as follows: Certification Exam Databricks Certified Professional Data Scientist Databricks Databricks-Databricks-Certified-Professional-Data-Scientist 3-952096989

Quiz

8/10
Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because
Select the answer
1 correct answer
A.
The penalty term is not differentiate
B.
The second derivative is not constant
C.
The objective function is not convex
D.
The constraints are quadratic Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum. You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.

Quiz

9/10
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......
Select the answer
1 correct answer
A.
Numerical
B.
Categorical
C.
Both 1 and 2 are correct
D.
None of the 1 and 2 are correct Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.

Quiz

10/10
Spam filtering of the emails is an example of
Select the answer
1 correct answer
A.
Supervised learning
B.
Unsupervised learning
C.
Clustering
D.
1 and 3 are correct
E.
2 and 3 are correct Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier: e-mails marked as spam or ham.
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