18 Questions
What is the primary purpose of a Decision Tree?
Representing features of a dataset
Which type of problems is a Decision Tree most commonly preferred for solving?
Classification problems
What do Leaf nodes represent in a Decision Tree?
Output of decisions
What does the Root node of a Decision Tree represent?
Entire dataset
Which algorithm is commonly used to build a Decision Tree?
CART algorithm
How does a Decision Tree split its branches?
Based on the test performed on dataset features
What type of methods are used as a preprocessing step in feature selection?
Filter methods
Which statistical test is typically used in filter methods to select features based on their correlation with the outcome variable?
Chi-squared test
What does Pearson’s correlation measure between two continuous variables?
Linear dependence
Which method is used to find a linear combination of features that characterizes or separates classes in a categorical variable?
Linear discriminant analysis (LDA)
What does ANOVA stand for and how does it differ from LDA?
Analysis of variance; Operated using categorical independent features and one continuous dependent feature
Which method involves finding a linear combination of features by penalizing the absolute size of the regression coefficients?
Lasso regression
What is the main concept behind the Random Forest algorithm?
Ensemble learning
In Random Forest, how are final predictions made?
By averaging predictions from multiple decision trees
What does the greater number of trees in a Random Forest lead to?
Higher accuracy
What is the purpose of ensemble learning in machine learning?
Combining multiple classifiers to improve model performance
Which technique is Random Forest primarily based on?
Decision trees
What problem does having a large number of trees in a Random Forest help to prevent?
Overfitting
Learn about Decision Tree, a supervised learning technique used for both classification and regression problems. Understand how decision nodes and leaf nodes are used to make predictions based on features and decision rules.
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