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stira [4]
2 years ago
9

I watched an ad but the im button done is no where in sight, has anyone else experienced this recently. it was fine a few days a

go (30 points)
Computers and Technology
1 answer:
Maurinko [17]2 years ago
7 0

Answer:

This is usually just a small glitch that happens sometimes when you are seeing an ad and wanting to skip. Simple fixes to this problem include refreshing the browser or closing the application that you are in. This also would happen to me but following this fix should help with the problem.

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Create a function named first_a that uses a list comprehension. The function will take a single integer parameter n. Find every
aivan3 [116]

Answer:

def first_a(n):

   lst1 =[x for x in range(1,n+1)if x%6==0 or x%11 ==0]

   print(lst1)

Explanation:

Above is a function in python programming language. We have used list comprehension to check numbers that are multiples of 6 or 11 in a range.

When this function is called it will receive an argument (n) of type integer, a range will then be generated from 1 to n+1 since n is inclusive. The modulo operator is used to determine is a value is a multiple of 6 or 11, since their multiples will evaluate to 0

3 0
3 years ago
Which item is not part of a college application
olga_2 [115]

Answer:

Explanation:

application form and any fees

4 0
3 years ago
In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will us
Arisa [49]

Answer:

Check the explanation

Explanation:

Lasso: R example

To run Lasso Regression you can re-use the glmnet() function, but with the alpha parameter set to 1.

# Perform 10-fold cross-validation to select lambda --------------------------- lambdas_to_try <- 10^seq(-3, 5, length.out = 100) # Setting alpha = 1 implements lasso regression lasso_cv <- cv.glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = TRUE, nfolds = 10) # Plot cross-validation results plot(lasso_cv)

Best cross-validated lambda lambda_cv <- lasso_cv$lambda.min # Fit final model, get its sum of squared residuals and multiple R-squared model_cv <- glmnet(X, y, alpha = 1, lambda = lambda_cv, standardize = TRUE) y_hat_cv <- predict(model_cv, X) ssr_cv <- t(y - y_hat_cv) %*% (y - y_hat_cv) rsq_lasso_cv <- cor(y, y_hat_cv)^2 # See how increasing lambda shrinks the coefficients -------------------------- # Each line shows coefficients for one variables, for different lambdas. # The higher the lambda, the more the coefficients are shrinked towards zero. res <- glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = FALSE) plot(res, xvar = "lambda") legend("bottomright", lwd = 1, col = 1:6, legend = colnames(X), cex = .7)

Kindly check the Image below.

5 0
3 years ago
Which one of the following items would you be most likely to keep in a database ?
DerKrebs [107]
C.) Payroll records. These are the most extensive and data-intensive.
8 0
3 years ago
Which of the following best explains why algorithms are written in the design phase?
Annette [7]

Answer:

D. Algorithms need to be written in the design phase so they can be translated into code in the development phase.

Explanation:

5 0
3 years ago
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