How many models with different combinations of parameter values will get trained in the following code? from sklearn.model_selection import GridSearchCV from sklearn.linear_model import SGDRegressor from sklearn.datasets import load_diabetes X, y = load_diabetes(return_X_y=True) params = [ {'alpha': [0.01,0.1,1],'learning_rate': ['constant','optimal']}, {'loss' : ['squared_error', 'huber'], 'alpha': ↪ [0.0001,0.001],'learning_rate':['constant','invscaling']}] grid= GridSearchCV(estimator= SGDRegressor(), param_grid = params, scoring = 'neg_mean_squared_error', return_train_score=True, verbose = 2, n_jobs= -1 ) grid.fit(X,y)

Machine Learning Practice · Quiz 1 · 25 Feb 2024 · January 2024 term

Question 13: How many models with different combinations of parameter…

Question 13

+2 marksOne correct option

How many models with different combinations of parameter values will get trained in the following code?

python

1from sklearn.model_selection import GridSearchCV
2from sklearn.linear_model import SGDRegressor
3from sklearn.datasets import load_diabetes
4
5X, y = load_diabetes(return_X_y=True)
6
7params = [
8    {'alpha': [0.01,0.1,1],'learning_rate': ['constant','optimal']},
9    {'loss' : ['squared_error', 'huber'], 'alpha':
10    ↪ [0.0001,0.001],'learning_rate':['constant','invscaling']}]
11
12grid= GridSearchCV(estimator= SGDRegressor(),
13                   param_grid = params,
14                   scoring = 'neg_mean_squared_error',
15                   return_train_score=True,
16                   verbose = 2,
17                   n_jobs= -1
18                   )
19
20grid.fit(X,y)

A
10
B
12
C
14
D
16

Show answer

Correct answer

C
14

See the whole paper Take it as a mock test

Question 13 of 24 in the IIT Madras BS Machine Learning Practice (MLP) Quiz 1 paper sat on 25 Feb 2024, in the January 2024 term (IIT M DIPLOMA AN2 EXAM QDD2 25 Feb 2024). It carries 2 marks.

Question 13: How many models with different combinations of parameter…

Question 13

More questions from this paper