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Classical Data Analysis Day 3 Master in Big Data & A.I. Solutions

9 Questions

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Classical Data Analysis Day 3 Master in Big Data & A.I. Solutions

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Questions and Answers

What is the main benefit of using Polynomial Regression?

It requires a linear relationship between variables

It simplifies the model by reducing the number of features

It is computationally less expensive than linear regression

It can capture more complex relationships in the data (correct)

In terms of Bias vs Variance trade-off, what does high bias indicate?

Underfitting

Overfitting

Model is too complex

Model is too simple (correct)

What is the purpose of Regularization in machine learning models?

To introduce more noise into the data

To speed up the training process

To increase model complexity

To reduce model complexity to prevent overfitting (correct)

Which machine learning technique is suitable for handling non-linear decision boundaries?

Support Vector Machines (SVM) Signup and view all the answers

What is the key limitation of Linear Regression modeling?

It assumes a linear relationship between variables Signup and view all the answers

Which technique focuses on grouping data points based on their similarity?

Hierarchical Clustering Signup and view all the answers

In what kind of learning do we know the labels for the input data during training?

Supervised Learning Signup and view all the answers

Which technique is used for dimensionality reduction in machine learning?

Principal Component Analysis (PCA) Signup and view all the answers

What is the primary objective of Ensemble Methods in machine learning?

Improve model performance by combining multiple models Signup and view all the answers

Classical Data Analysis Day 3 Master in Big Data & A.I. Solutions

Choose a study mode

Podcast

Questions and Answers

What is the main benefit of using Polynomial Regression?

In terms of Bias vs Variance trade-off, what does high bias indicate?

What is the purpose of Regularization in machine learning models?

Which machine learning technique is suitable for handling non-linear decision boundaries?

What is the key limitation of Linear Regression modeling?

Which technique focuses on grouping data points based on their similarity?

In what kind of learning do we know the labels for the input data during training?

Which technique is used for dimensionality reduction in machine learning?

What is the primary objective of Ensemble Methods in machine learning?

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