Answer:
print("Let's play Silly Sentences!")
print(" ")
name=input("Enter a name: ")
adj1=input("Enter an adjective: ")
adj2=input("Enter an adjective: ")
adv=input("Enter an adverb: ")
fd1=input("Enter a food: ")
fd2=input("Enter another food: ")
noun=input("Enter a noun: ")
place=input("Enter a place: ")
verb=input("Enter a verb: ")
print(" ")
print(name + " was planning a dream vacation to " + place + ".")
print(name + " was especially looking forward to trying the local \ncuisine, including " + adj1 + " " + fd1 + " and " + fd2 + ".")
print(" ")
print(name + " will have to practice the language " + adv + " to \nmake it easier to " + verb + " with people.")
print(" ")
print(name + " has a long list of sights to see, including the\n" + noun + " museum and the " + adj2 + " park.")
Explanation:
Got it right. Might be a longer version, but it worked for me.
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.
Answer: i can't see the hole thing?
Explanation:
You cant, you have to either delete items off to gain some storage back or go buy an ipad with more storage
Answer:
Wired equivalent privacy standard has the significant disadvantages as, it is uses the static encryption keys. Wired equivalent privacy are the security and the privacy protocol which are used for designing the wireless local area network. And the static encryption key are used to set up the router for encrypting the packet in the device. The main disadvantage is that it enables the MAC address and it is easily detectable by the hackers.