Practical Data Science using Python - Decision Tree - Model Optimization using Grid Search Cross Validation

Practical Data Science using Python - Decision Tree - Model Optimization using Grid Search Cross Validation

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Hard

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The video tutorial explains the concept of hyperparameters in decision trees and introduces Grid Search CV with K-Fold Cross Validation as a method to optimize these parameters. It details the implementation process, including setting up the parameter grid and analyzing results. The tutorial also covers tuning multiple parameters simultaneously and evaluating the final model's performance.

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10 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

What are hyperparameters in the context of decision trees?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain the purpose of the 'Max features' hyperparameter.

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

What does the 'Max depth' parameter control in a decision tree?

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe the function of 'min samples split' in decision tree pruning.

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5.

OPEN ENDED QUESTION

3 mins • 1 pt

How does 'Grid search CV' help in optimizing hyperparameters?

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6.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the significance of 'K Fold cross validation' in model training?

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7.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain how the results of the grid search CV can be interpreted.

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