Machine Learning for Industrial Engineering: Classification Trees

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Questions and Answers

What effect does pruning a regression tree have on the number of leaves?

The number of leaves increases significantly

The number of leaves is unaffected by pruning

The number of leaves decreases significantly (correct)

The number of leaves remains unchanged

What is the main idea behind bagging and random forest?

To fit a single complex tree to the data

To prune trees to reduce complexity

To calculate predictor importance

To fit many weak trees to limit overfitting and improve performance (correct)

What is the main difference between bagging and random forest?

The number of trees fitted

The complexity of the trees

The number of features used for selection at each split (correct)

The type of data used

What happens to the performance of a model when using bagging and random forest compared to a single deeper tree?

The performance improves significantly Signup and view all the answers

What can be calculated after pruning a regression tree?

Predictor importance Signup and view all the answers

What is the purpose of cross-validation in regression trees?

To limit overfitting and improve performance Signup and view all the answers

What is cost complexity pruning in regression trees?

A method to prune trees based on cost and complexity Signup and view all the answers

How long does the pruning process take according to the text?

Around 30 seconds Signup and view all the answers

What is the purpose of pruning a decision tree in machine learning?

To improve the performance of the tree Signup and view all the answers

What is the benefit of cross-validation in machine learning?

It reduces overfitting Signup and view all the answers

What is the output of the cost-complexity pruning path in decision trees?

A Python dictionary of α values and impurity measures Signup and view all the answers

What is the purpose of calculating metrics for all folds in cross-validation?

To calculate the average accuracy Signup and view all the answers

What is the difference between the accuracy calculated with cross-validation and with a validation set?

The accuracy is lower with cross-validation Signup and view all the answers

What is the idea behind tree pruning in decision trees?

Grow a deep tree and prune it Signup and view all the answers

Why is max_depth not set as a limit for the tree's complexity in the pruning process?

To allow the tree to grow as deep as possible Signup and view all the answers

What is the purpose of applying cost-complexity pruning to a decision tree?

To reduce the complexity of the tree Signup and view all the answers

What is the criterion used in regression trees?

Mean Squared Error Signup and view all the answers

How is the predictor importance calculated in regression trees?

According to the variance explanation due to splits of the tree Signup and view all the answers

What is the purpose of cross-validation in regression trees?

To get the cross validated test error Signup and view all the answers

What is the first step in pruning a regression tree?

Grow a deep tree with no restrictions for the depth Signup and view all the answers

What is the next step after getting the alphas in cost complexity pruning?

Do the exhaustive search Signup and view all the answers

What is the purpose of pruning a regression tree?

To reduce the complexity of the tree Signup and view all the answers

What is the output of the cross-validation step in regression trees?

Cross validated test error Signup and view all the answers

What is the importance of using the same tree configurations in cross-validation?

To compare the validation set error calculated before with the cross validated error Signup and view all the answers

Study Notes

User-Defined Functions

Classification trees use cross-validation to calculate the cross-validated error, and the metrics are calculated for all folds, then averaged.

Tree Pruning

Tree pruning involves growing a deep tree and then pruning it to improve its performance.
The unpruned tree's metrics are calculated to compare before and after pruning.
Cost-complexity pruning is applied to the tree, resulting in a Python dictionary object including the α values and their corresponding impurity measures.

Regression Trees

Regression trees are used in a regression setting, and the Concrete data is used.
The criterion used is 'squared_error', and other possible arguments can be accessed through online documentation.
Predictor importance is calculated and plotted based on the variance explanation due to splits of the tree.

Regression Trees - Cross Validation

Cross-validation is used to get the cross-validated test error, similar to the classification setting.
The same tree configurations (max_depth,…) should be used to compare the validation set error with the cross-validated error.

Regression Trees - Pruning

Pruning involves growing a deep tree with no restrictions for the depth, resulting in a large number of leaves.
Cost-complexity pruning is applied, involving getting the alphas and then doing an exhaustive search.

Bagging and Random Forest

The main idea of bagging and random forest is to fit many weak trees to limit overfitting and improve performance.
The main difference between bagging and random forest is the number of features (predictors) used for selection at each split.
In bagging, all features are selected, while in random forest, the selection is limited.
The max_features is set as the number of columns in the predictors' data frame.
Bagging and random forest show a significant improvement in performance compared to a single deeper tree.
Predictors' importance can be calculated and plotted.

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Description

This quiz covers classification trees and cross-validation in machine learning, specifically for industrial engineering applications. It assesses understanding of model assessment metrics and user-defined functions