FUNDAMENTALS OF COMPUTER

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DATABASE FUNDAMENTALS

BASICS OF BIG DATA

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Imagine you are working on a project which is a binary classification problem. You trained a model on training dataset and get the below confusion matrix on validation datasetBased on the above confusion matrix, choose which option(s) below will give you correct predictions?1. Accuracy is  0.912. Misclassification rate is   0.913. False positive rate is  0.954. True positive rate is  0.95
A
1 and 3
B
1 and 4
C
2 and 4
D
2 and 3
Explanation: 

Detailed explanation-1: -Machine Learning algorithm to be used purely depends on the type of data in a given dataset.

Detailed explanation-2: -The smaller the training data set, the lower the test accuracy, while the training accuracy remains at about the same level.

Detailed explanation-3: -1 Answer. When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit. For example, Naives bayes works best when the training set is large.

Detailed explanation-4: -Now, to use which algorithm depends on the objective of the business problem. If inference is the goal, then restrictive models are better as they are much more interpretable. Flexible models are better if higher accuracy is the goal. In general, as the flexibility of a method increases, its interpretability decreases.

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