Table of Contents
- 1 What is an advantage of using decision trees over other machine learning methods?
- 2 How can you improve the accuracy of a decision tree model?
- 3 What are the benefits of decision tree?
- 4 Why correlation is better than mutual information?
- 5 What is mutual information in statistics?
- 6 How do you calculate mutmutual information?
What is an advantage of using decision trees over other machine learning methods?
Compared to other algorithms decision trees requires less effort for data preparation during pre-processing. A decision tree does not require normalization of data. A decision tree does not require scaling of data as well.
How can you improve the accuracy of a decision tree model?
8 Methods to Boost the Accuracy of a Model
- Add more data. Having more data is always a good idea.
- Treat missing and Outlier values.
- Feature Engineering.
- Feature Selection.
- Multiple algorithms.
- Algorithm Tuning.
- Ensemble methods.
Why might you use a decision tree rather than a decision table?
Decision Table is just a tabular representation of all conditions and actions. Decision Trees are always used whenever the process logic is very complicated and involves multiple conditions.
What are advantages of using decision trees?
Decision trees provide an effective method of Decision Making because they:
- Clearly lay out the problem so that all options can be challenged.
- Allow us to analyze fully the possible consequences of a decision.
- Provide a framework to quantify the values of outcomes and the probabilities of achieving them.
What are the benefits of decision tree?
Why correlation is better than mutual information?
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much “knowledge” one can gain of a certain variable by knowing the value of another variable.
What is the difference between mutual information and information gain?
Information gain is calculated by comparing the entropy of the dataset before and after a transformation. Mutual information calculates the statistical dependence between two variables and is the name given to information gain when applied to variable selection.
How to find the mutual information between two random variables?
The mutual information between two random variables X and Y can be stated formally as follows: I(X ; Y) = H(X) – H(X | Y) Where I(X ; Y) is the mutual information for X and Y, H(X) is the entropy for X and H(X | Y) is the conditional entropy for X given Y. The result has the units of bits.
What is mutual information in statistics?
3 Mutual Information Mutual information is a quantity that measures a relationship between two random variables that are sampled simultaneously. In particular, it measures how much information is communicated, on average, in one random variable about another. Intuitively, one might ask, how much does one random variable tell me about another?
How do you calculate mutmutual information?
Mutual information is calculated between two variables and measures the reduction in uncertainty for one variable given a known value of the other variable. A quantity called mutual information measures the amount of information one can obtain from one random variable given another.