Below are the 8 actual target variable values in the training file.

[0,0,0,1,1,1,1,1] What is the entropy of the target variable?

Which of the following statement(s) is / are true for Gradient Descent (GD) and Stochastic Gradient Descent (SGD)?

1. In GD and SGD, you update a set of parameters in an iterative manner to minimize the error function.
2. In SGD, you must run through all the samples in your training set for a single parameter update in each iteration.
3. In GD, you either use the entire data points or a subset of training data to update a parameter in each iteration.

What is an End-to-End project?

Which type of learning does the Control problem resolve? 

How will we tell whether the project is successful or not in CRISP-DM?

Can we achieve better performance with more data or better

preprocessing in CRISP-DM?

Are there any computational challenges with deploying the

model into production in CRISP-DM?

Have we picked the optimal hyperparameters for the model in CRISP-DM?

Are there any formatting issues we should address before modeling in CRISP-DM?

Are there any human errors that need corrections in CRISP-DM?