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From exercise 1,
(a) Compute the Sharpe ratio of the resulting in-sample forecasts, from point 1.a (see Chapter 14 for a definition of Sharpe ratio).
(b) Repeat point 1.a, this time with accuracy as the scoring function. Compute the in-sample forecasts derived from the hyper-tuned parameters.
(c) What scoring method leads to higher (in-sample) Sharpe ratio?
exercise 1
Using the function 
from Chapter 8, form a synthetic dataset of 10,000 observations with 10 features, where 5 are informative and 5 are noise.
(a) Use 
to find the C, gamma optimal hyperparameters on a SVC with RBF kernel, where 
and the scoring function is 
(b) How many nodes are there in the grid?
(c) How many fits did it take to find the optimal solution?
(d) How long did it take to find this solution?
(e) How can you access the optimal result?
(f) What is the CV score of the optimal parameter combination?
(g) How can you pass sample weights to the SVC?
