Answer:
continual user involvement gives the flexibility to analyze the requirements in right direction. because there is continuous meetings with the end user and he can provide right direction or avoids wrong interpretation of the requirement
Explanation:
continual user involvement is useful when we are following agile methodology where we are building complex systems. it is not useful for simple sytems and following waterfall methodology
1. Less picture less distraction, but where to put pictures ? At the start of your presentation just to initially get the attention of the audience.
2. Use bullets for texts, never use paragraphs. Use short text or even words only which captures the subject of the presentation.
3. In conjunction with point 1. Use less animations as well. Especially for formal presentations
Additionally : Make sure to know your audience, for professionals, make the presentations short and precise. For much informal audiences you can be playful but always keep in mind not to overshadow the attention from the speaker.
Answer:
int i = 0; i < names.size(); i++
Explanation:
The ArrayList must be read in the forward direction, and it is going to start from 0 certainly. Also, the iteration is going to end when i is exactly one less than the size of the ArrayList. And this is possible only if we choose the option mentioned in the Answer section. In this, i starts from 0 and iterates till i is one less than name.size() which is the size of the ArrayList.
Answer:
b
Explanation:
First, we need to initialize the classifier.
Then, we are required to train the classifier.
The next step is to predict the target.
And finally, we need to evaluate the classifier model.
You will find different algorithms for solving the classification problem. Some of them are like decision tree classification etc.
However, you need to know how these classifier works. And its explained before:
You need to initialize the classifier at first.
All kinds of classifiers in the scikit-learn make use of the method fit(x,y) for fitting the model or the training for the given training set in level y.
The predict(x) returns the y which is the predicted label.And this is prediction.
For evaluating the classifier model- the score(x,y) gives back the certain score for a mentioned test data x as well as the test label y.
