Machine Learning: Bias VS Variance

The video tutorial discusses the importance of balancing bias and variance in machine learning models. It explains supervised learning, the bias-variance tradeoff, and how model complexity affects performance. Techniques like regularization are introduced to optimize models and prevent overfitting or underfitting.

What is the main goal of supervised learning in machine learning?

• To predict the correct output from given input

• To memorize the training data

• To minimize the number of data points

• To increase the complexity of the model

Why is it problematic to use the same data for training and testing a model?

• It increases the noise in the data

• It reduces the model's complexity

• It causes the model to overfit

• It leads to a high variance

What does the bias in a machine learning model represent?

• The complexity of the model

• The error due to noise

• The spread of data points

• The consistent error in predictions

How is variance in a model's predictions characterized?

• By the noise in the data

• By the model's ability to generalize

• By the spread of predictions around the target

• By the model's complexity

What happens when a model is too complex?

• It has high bias

• It captures noise and overfits

• It underfits the data

• It reduces the number of parameters

What is the purpose of regularization in machine learning?

• To increase the number of learnable parameters

• To prevent overfitting by simplifying the model

• To reduce the model's bias

• To increase the model's complexity

Which technique is used to regularize a neural network?

• Adding more data points

• Pruning

• Dropouts

• Increasing polynomial degree