Answer:
Training data is used to fine-tune the algorithm’s parameters and evaluate how good the model is
Explanation:
The statement about datasets used in Machine Learning that is NOT true is "Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is."
This is based on the fact that a Training dataset is a process in which a dataset model is trained for corresponding it essentially to fit the parameters.
Also, Testing the dataset is a process of examining the performance of the dataset. This refers to hidden data for which predictions are determined.
And Validation of dataset is a process in which results are verified to perfect the algorithm's details or parameters
Answer:
There is also an attachment below
Explanation:
Since we are talking about binary search, let's assume that the items are sorted according to some criteria.
Time complexity of binary search is O(logN) in worst case, best case and average case as well. That means it can search for an item in Log N time where N is size of the input. Here problem talks about the item not getting found. So, this is a worst case scenario. Even in this case, binary search runs in O(logN) time.
N = 700000000.
So, number of comparisions can be log(N) = 29.3 = 29.
So, in the worst case it does comparisions 29 times
Answer:
The golang control flow statements are used to break the flow of execution by branching, looping, decision making statements by enabling the program to execute code based on the conditions. All programmers must know the control flows like if-else, switch case, for loop, break, continue, return.
Explanation:
