How do random forests overcome the principal weakness of a decision tree
Added by Melissa B.
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Decision trees are prone to overfitting, which means they can create overly complex models that capture noise in the training data rather than the underlying patterns. This leads to poor generalization on unseen data. Show more…
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Question 1 Which of the following are advantages to using decision trees over other models? (Select all that apply) - Trees are easy to interpret and visualize - Trees are naturally resistant to overfitting - Trees often require less preprocessing of data Question 2 What is the main reason that each tree of a random forest only looks at a random subset of the features when building each node? - To improve generalization by increasing the diversity among the trees and making the model more robust to overfitting.
T. L.
A random forest is an ensemble learning method that attempts to decrease the bias of decision trees. An infinite depth binary point is mislabeled decision tree can always achieve 100% training accuracy, provided that no outliers are present. K-Means clustering looks to find low-dimensional representation of the observations that explain a good fraction of the variance. Linear SVMs, widening the margin increases the number of observations that violate the margin of the classifier. A natural spline is a regression spline with the additional constraints that the function is required to be linear at the boundaries.
Tanvi G.
vii. Which of the following statement is NOT true? a) A decision tree is easy to understand b) Regression model overfits the data c) Variances of random forest are less than a decision tree d) Random forest is used for learning complex non-linear relationships viii. What will be entropy value of a training set that contains equal number of positive and negative classes? a. 0 b. 0.5 c. 1 d. None of the above ix. What performance measure should be used to evaluate a decision tree? a. Confusion matrix b. F1 score c. RMSE, MAPE, or MAD d. Depends on the dependent variable x. What performance measure can be used to evaluate a logistic regression model? a. confusion matrix b. R² c. RMSE, MAPE, or MAD d. Depends on the dependent variable
Sri K.
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