What is Hyper Parameter Tuning? (Python, Scikit Learn, Keras) | Deep Learning Tutorial 16

Q: What is the significance of hyperparameters in model training?

A: Hyperparameters play a crucial role in model training as they are parameters that are not learned during the training process but directly impact the learning process and model performance.

Q: Explain the concept of early stopping in model training.

A: Early stopping is a technique used to prevent overfitting by stopping the training process before the model's performance on a validation dataset starts to degrade, typically by monitoring a metric like validation loss.

Q: Can you differentiate between underfitting, overfitting, and best fitting in the context of model training?

A: Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, overfitting happens when a model is too complex and captures noise in the training data, while best fitting represents a model that adequately captures the patterns without underfitting or overfitting.

Q: What is the process of hyperparameter tuning and why is it important?

A: Hyperparameter tuning involves adjusting the hyperparameters of a model to optimize its performance. It is crucial because proper tuning can significantly improve a model's accuracy and generalization on unseen data.

Q: What are the three types of gradient descent and how do they differ?

A: The three types of gradient descent are batch gradient descent, mini-batch gradient descent, and stochastic gradient descent. They differ in how they update the model's parameters based on the gradients calculated from a batch, a subset of data, or a single data point, respectively.

Q: How does the batch size affect training data set and model accuracy?

A: The batch size determines the number of samples used in each iteration of training. A smaller batch size offers faster convergence and more noise in the updates, while a larger batch size provides a more stable gradient estimate but may slow down the training process.

Q: Why is it important to address overfitting in machine learning models?

A: Overfitting can lead to a model performing well on the training data but poorly on unseen data, thus reducing its generalization capabilities. Addressing overfitting through techniques like early stopping helps improve model performance on unseen data.