Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - Comparing Different Machine Learning Models

Why is there no shortcut to choosing the right machine learning model?

• Because machine learning is a field of philosophy

• Because models are always plug and play

• Because understanding algorithms and their contexts is crucial

• Because all models are equally powerful

What is a key advantage of linear models?

• They are always non-linear

• They can handle large datasets effortlessly

• They are easy to interpret

• They are always the most powerful

Which of the following is NOT a basic non-linear model?

• K Nearest Neighbor

• Decision Trees

• Support Vector Machine

• Naive Bayes

What makes ensemble models like Random Forests powerful?

• They use a single decision tree

• They are always linear

• They require no data preprocessing

• They average predictions from multiple trees

Why are Support Vector Machines (SVMs) less commonly used today?

• They are too expensive to implement

• They are always outperformed by linear models

• They do not scale well with large datasets

• They are too simple

What is a major challenge of using deep learning models?

• They require specialized libraries and hardware

• They are too easy to interpret

• They are not suitable for complex tasks

• They are always faster to train

What is the recommended approach to understanding how a model works in a specific situation?

• Always use the most complex model

• Experiment and observe the results

• Rely on philosophical discussions

• Consult a machine learning oracle