APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Factor analysis
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Decision trees are robust to outliers
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Decision trees are prone to be overfit
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None of the above
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Detailed explanation-1: -2. Decision trees are prone to overfitting, especially when a tree is particularly deep. This is due to the amount of specificity we look at leading to smaller sample of events that meet the previous assumptions. This small sample could lead to unsound conclusions.
Detailed explanation-2: -Among the most common and prominent disadvantages of decision trees are that it’s a high variance algorithm. This means that it can easily overfit because it has no inherent mechanism to stop, thereby creating complex decision rules.
Detailed explanation-3: -Apart from overfitting, Decision Trees also suffer from following disadvantages: 1. Tree structure prone to sampling – While Decision Trees are generally robust to outliers, due to their tendency to overfit, they are prone to sampling errors.
Detailed explanation-4: -Overfitting is one of the practical difficulties for decision tree models. Decision trees cannot be used well with continuous numerical variables. A small change in the data tends to cause a big difference in the tree structure, which causes instability. More items •24-Dec-2020