- 1 Which split is better if we use entropy as the impurity measure?
- 2 Why do we prefer information gain over accuracy when splitting in decision tree?
- 3 Why is entropy better than gini?
- 4 How will you counter overfitting in the decision tree?
- 5 Can information gain be greater than 1?
- 6 Which of the following is a disadvantage of decision tree?
- 7 What happens if we use entropy as an impurity metric?
- 8 How is the entropy of a split calculated?
Which split is better if we use entropy as the impurity measure?
Entropy v/s Gini Impurity: The internal working of both methods is very similar and both are used for computing the feature/split after every new splitting. But if we compare both the methods then Gini Impurity is more efficient than entropy in terms of computing power.
Why do we prefer information gain over accuracy when splitting in decision tree?
Q12) Why do we prefer information gain over accuracy when splitting? Q13) Random forests (While solving a regression problem) have the higher variance of predicted result in comparison to Boosted Trees (Assumption: both Random Forest and Boosted Tree are fully optimized).
Does entropy gain information?
The information gain is the amount of information gained about a random variable or signal from observing another random variable. Entropy is the average rate at which information is produced by a stochastic source of data, Or, it is a measure of the uncertainty associated with a random variable.
Which algorithm uses information gain as splitting criteria?
Information gain can be used as a split criterion in most modern implementations of decision trees, such as the implementation of the Classification and Regression Tree (CART) algorithm in the scikit-learn Python machine learning library in the DecisionTreeClassifier class for classification.
Why is entropy better than gini?
Conclusions. In this post, we have compared the gini and entropy criterion for splitting the nodes of a decision tree. On the one hand, the gini criterion is much faster because it is less computationally expensive. On the other hand, the obtained results using the entropy criterion are slightly better.
How will you counter overfitting in the decision tree?
There are several approaches to avoiding overfitting in building decision trees.
- Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set.
- Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.
How do you determine the best split in decision tree?
Decision Tree Splitting Method #1: Reduction in Variance
- For each split, individually calculate the variance of each child node.
- Calculate the variance of each split as the weighted average variance of child nodes.
- Select the split with the lowest variance.
- Perform steps 1-3 until completely homogeneous nodes are achieved.
How do you gain information?
Information Gain is calculated for a split by subtracting the weighted entropies of each branch from the original entropy. When training a Decision Tree using these metrics, the best split is chosen by maximizing Information Gain.
Can information gain be greater than 1?
Yes, it does have an upper bound, but not 1. The mutual information (in bits) is 1 when two parties (statistically) share one bit of information. However, they can share a arbitrary large data. In particular, if they share 2 bits, then it is 2.
Which of the following is a disadvantage of decision tree?
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.
What are the issues in decision tree learning how they are overcome?
The weaknesses of decision tree methods : Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.
Is the Gini impurity and information gain entropy the same?
Gini impurity and Information Gain Entropy are pretty much the same. And people do use the values interchangeably. Below are the formulae of both: Gini: G i n i (E) = 1 − ∑ j = 1 c p j 2
What happens if we use entropy as an impurity metric?
Next, let’s see what happens if we use Entropy as an impurity metric: In contrast to the average classification error, the average child node entropy is not equal to the entropy of the parent node. Thus, the splitting rule would continue until the child nodes are pure (after the next 2 splits).
How is the entropy of a split calculated?
Entropy can be defined as a measure of the purity of the sub split. Entropy always lies between 0 to 1. The entropy of any split can be calculated by this formula. The algorithm calculates the entropy of each feature after every split and as the splitting continues on, it selects the best feature and starts splitting according to it.
How is the entropy of a mixed class calculated?
A set of many mixed classes is unpredictable: a given element could be any color! This would have high entropy. The actual formula for calculating Information Entropy is: Information Gain is calculated for a split by subtracting the weighted entropies of each branch from the original entropy.