Q.1. Explain Learning rate as a hyperparameter.Q.2. What are eigen vectors?Q.3. What is cross-validation?Q.4. What are basic steps to build a machine learning model?Q.5. What are shrinkage methods?

Q.1. Learning rate as a hyperparameter: The learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. It decides how quickly the model is adapted to the problem. Smaller learning rates require more training epochs given the smaller changes made to the weights each update, while larger learning rates result in rapid changes and require fewer training epochs.

Q.2. Eigen vectors: In linear algebra, an eigenvector or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue is the factor by which the eigenvector is scaled.

Q.3. Cross-validation: Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. It provides a more accurate measure of model prediction performance by reducing the variance of a single trial of a train/test split.

Q.4. Basic steps to build a machine learning model:

1. Define the problem
2. Prepare the data
3. Evaluate algorithms
4. Improve results
5. Present results

Q.5. Shrinkage methods: Shrinkage methods, also known as regularization methods, are a form of regression that involves constraining or regularizing or shrinking the coefficient estimates towards zero. They can significantly reduce the variance of the model, without substantial increase in its bias. Examples of shrinkage methods are Ridge Regression and Lasso Regression.