Machine Learning MCQ - Type of nodes in a decision tree

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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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Type of nodes in a decision tree

Which is a decision node?

Can we have labels in internal nodes in a decision tree?

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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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What is the impact of high false positive in precision ?

When do we need to reduce the detection of false positives in a prediction system?

When you like to have high precision what should be the value of false positive recognitions?

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False positives and high precision are inversely proportiional

List few examples of false positive recognition