Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
Answer:
Check the explanation
Explanation:
We can utilize the above algorithm with a little in modification. If in each of the iteration, we discover a node with no inward edges, then we we’re expected succeed in creating a topological ordering.
If in a number of iteration, it becomes apparent that each of the node has a minimum of one inward edge, then there must be a presence of cycle in the graph.
So our algorithm in finding the cycle is this: continually follow an edge into the node we’re presently at (which is by choosing the first one on the adjacency list of inward edges to decrease the running time).
Since the entire node has an inward edge, we can do this continually or constantly until we revisit a node v for the first time.
The set of nodes that we will come across among these two successive visits is a cycle (which is traversed in the reverse direction).
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Its the purpose, not the audience the other idiot that answered the question is mad wrong
Answer:
C. Timmy can play the game at a higher level of visual detail if his computer has an integrated video card.
Explanation:
The processor efficiency and memory requirements are much more necessary to get the game to run and give it a playable framerate. Having a video card will improve his graphics, so this is more of a suggestion than a necessity.
