Data Science and Machine Learning (Theory and Projects) A to Z - Hands-on Machine Learning Project Using Scikit-Learn: C

The video tutorial discusses model selection, focusing on the degree of polynomial as a hyperparameter. It explains how cross-validation can help determine the best model by iterating over different polynomial degrees. The tutorial demonstrates data generation, implementation of cross-validation using scikit-learn, and plotting of training and validation scores. It also explores the impact of different cross-validation splits on model performance. The video concludes with a brief mention of the next topic, face recognition.

What is a hyperparameter in the context of model selection?

• A parameter that is irrelevant to the model

• A parameter that is set before the learning process

• A parameter that is learned from the data

• A parameter that is always fixed

Why is cross-validation important in model selection?

• It increases the complexity of the model

• It reduces the size of the dataset

• It determines the best hyperparameter by testing different models

• It helps in visualizing the data

What is the purpose of generating synthetic data in this context?

• To avoid using any data at all

• To have a controlled environment for testing model performance

• To test the model on real-world data

• To increase the noise in the data

Which library is used for implementing cross-validation in this tutorial?

• PyTorch

• TensorFlow

• Keras

• Scikit-learn

What is the role of the validation curve in cross-validation?

• To reduce the noise in the data

• To generate synthetic data

• To compute the training and validation scores

• To plot the data points

What issue was encountered during the plotting of the validation curve?

• The curve was too complex

• The curve was plotted in the wrong color

• The curve was not plotted due to dimensionality issues

• The curve was not smooth

How does the training score change as the polynomial degree increases?

• It fluctuates randomly

• It remains constant

• It decreases

• It increases

What was observed about the validation score at higher polynomial degrees?

• It increased

• It remained constant

• It fluctuated randomly

• It decreased

What effect does changing the cross-validation split have on the results?

• It only affects the training score

• It makes the model more complex

• It can significantly alter the validation score

• It has no effect

What future topic is hinted at the end of the video?

• Natural language processing

• Reinforcement learning

• Face recognition application

• Time series analysis