Podcast
Questions and Answers
What is the primary consequence of high bias in a machine learning model?
What is the primary consequence of high bias in a machine learning model?
- The model is only able to learn linear relationships
- The model becomes overly complex and prone to overfitting
- The model's performance is highly dependent on the training dataset
- The model is unable to learn precisely from its training data (correct)
Which of the following is a common strategy for reducing high bias in a deep learning model?
Which of the following is a common strategy for reducing high bias in a deep learning model?
- Decreasing the learning rate
- Increasing the size of the neural network (correct)
- Increasing the number of training samples
- Decreasing the number of hidden layers
What is the primary difference between a model with high bias and a model with high variance?
What is the primary difference between a model with high bias and a model with high variance?
- A model with high bias is prone to overfitting, while a model with high variance is unable to learn from the data
- A model with high bias is unable to learn from the data, while a model with high variance is prone to overfitting
- A model with high bias is too simple, while a model with high variance is too complex (correct)
- A model with high bias is too complex, while a model with high variance is too simple
What is the effect of underfitting on a model's performance?
What is the effect of underfitting on a model's performance?
What is the primary benefit of trying different architectures in deep learning?
What is the primary benefit of trying different architectures in deep learning?
What is the primary goal of manipulating the neural network structure in deep learning?
What is the primary goal of manipulating the neural network structure in deep learning?
What is the primary purpose of the validation set in neural network training?
What is the primary purpose of the validation set in neural network training?
What happens when the validation set is not from the same distribution as the test set?
What happens when the validation set is not from the same distribution as the test set?
What is the primary goal of gradient descent in neural network training?
What is the primary goal of gradient descent in neural network training?
What is the difference between parameters and hyper-parameters in neural network training?
What is the difference between parameters and hyper-parameters in neural network training?
What is the consequence of overfitting in neural network training?
What is the consequence of overfitting in neural network training?
Why is it essential to avoid data leakage in neural network training?
Why is it essential to avoid data leakage in neural network training?
What is the term for the condition where a model performs well on the training set but fails to generalize on new data?
What is the term for the condition where a model performs well on the training set but fails to generalize on new data?
In the context of model performance, what is high variance indicative of?
In the context of model performance, what is high variance indicative of?
What is the trade-off typically considered when trying to address high variance and bias in machine learning models?
What is the trade-off typically considered when trying to address high variance and bias in machine learning models?
How can overfitting be reduced by modifying the dataset?
How can overfitting be reduced by modifying the dataset?
What characterizes a model that exhibits low variance and low bias?
What characterizes a model that exhibits low variance and low bias?
Why is collecting more data often suggested as a method to avoid overfitting?
Why is collecting more data often suggested as a method to avoid overfitting?
