Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST dumps - 100% Pass Guarantee!

Correct Answer: ABCDE

Explanation: During the discovery phase you need to find how much resources are required as early as possible and for that even you can involve various stakeholders like Software engineering team, DBAs,

Network engineers, System administrators etc. for your requirement and these resources are already available or you need to procure them. Also, what would be source of the data? What all tools and software's are required to execute the

same?

Correct Answer: ABC

Explanation: naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters

Correct Answer: B

Explanation: Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

Correct Answer: A

Explanation: 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.

Correct Answer: A

From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C) P(B,C) =P(A|BC) This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)