FUNDAMENTALS OF COMPUTER

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DATABASE FUNDAMENTALS

BASICS OF BIG DATA

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Identify the correct statement(s) in context of overfitting in decision trees:Statement I:The idea of Post-pruning is to grow a tree to its maximum size and then remove the nodes using a top-bottom approach.Statement II:The idea of Pre-pruning is to stop tree induction before a fully grown tree is built, that perfectly fits the training data.
A
Only Statement I is true
B
Only Statement 2 is true
C
Both Statements are true
D
Both Statements are false
Explanation: 

Detailed explanation-1: -Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data.

Detailed explanation-2: -Pruning refers to a technique to remove the parts of the decision tree to prevent growing to its full depth. By tuning the hyperparameters of the decision tree model one can prune the trees and prevent them from overfitting. There are two types of pruning Pre-pruning and Post-pruning.

Detailed explanation-3: -Underfitting means that your model makes accurate, but initially incorrect predictions. In this case, train error is large and val/test error is large too. Overfitting means that your model makes not accurate predictions. In this case, train error is very small and val/test error is large.

Detailed explanation-4: -Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data. When data scientists use machine learning models for making predictions, they first train the model on a known data set.

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