Tuesday, July 26, 2022

Machine Learning MCQ - Type of nodes in a decision tree

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, decision tree, types of nodes, root, decision node, leaft node, how many incoming edges are there in an internal node?

Machine Learning MCQ - Types of nodes in a decision tree

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1. What are different types of nodes a decision tree has?

a) Root node

b) Internal nodes

c) Leaf nodes

d) All of the above

Answer: (d) All of the above

A decision tree has all the three nodes.

Root node – node with NO incoming edges and ZERO or more outgoing edges. It contains attribute test conditions to separate records.

Internal nodes – nodes with exactly ONE incoming edge and ZERO or more outgoing edges. Internal nodes contain attribute test conditions to separate records.

Leaf (terminal) nodes – nodes with exactly ONE incoming edge with NO outgoing edges. Leaf nodes have class labels.


Decision tree nodes - an example




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Sunday, July 24, 2022

Natural Language Processing MCQ - Impact of FALSE POSITIVE on precision

Multiple choices questions in NLP, Natural Language Processing solved MCQ, Accuracy and precision, How does high false positive value affect precision of the prediction system? false positive is type I error, Why does a prediction system with high false positives ends up with low precision?

Natural Language Processing MCQ - Impact of FALSE POSITIVE on the system's precision mesure

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1. A classifier that makes a lot of FALSE POSITIVEs will have ________.

a) Low recall

b) Low precision

c) High recall

d) High precision


Answer: (b) Low precision

What is False Positive (FP)?

False positive is when the system (classifier) incorrectly classifies a wrong event as correct (positive) class. In other words, how many negative cases get incorrectly identified as positive. It is also referred as TYPE I error.

Let us assume that we have developed a house alarm security system to automatically detect the presence of intruders. FALSE POSITIVE is, your alarm goes off when there is no intruder.

Few other examples;

  • A diagnosis system detects that a patient is affected with a disease which he actually not.
  • A fire alarm system alarms when actually there is no fire.
  • A financial system detects suspicious transactions which are actually genuine.


PRECISION is calculated as follows;

Precision = TP / (TP + FP)

If the system detects more FPs, it lowers the PRECISION of the underlying system. Hence, while building classification models, you may have to choose to build models with lower false positives if a high precision score is desirable as per your requirements.      




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