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Alekssandra [29.7K]
3 years ago
8

What are the 6 external parts of a computer system

Computers and Technology
1 answer:
denis23 [38]3 years ago
4 0
1. Monitor
2.C.P.U
3.Mouse
4. Keyboard
5. Printer
6. Speaker
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Question 6 Which of the following statements about datasets used in Machine Learning is NOT true? 1 point Testing data is data t
Inessa [10]

Answer:

Training data is used to fine-tune the algorithm’s parameters and evaluate how good the model is

Explanation:

The statement about datasets used in Machine Learning that is NOT true is "Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is."

This is based on the fact that a Training dataset is a process in which a dataset model is trained for corresponding it essentially to fit the parameters.

Also, Testing the dataset is a process of examining the performance of the dataset. This refers to hidden data for which predictions are determined.

And Validation of dataset is a process in which results are verified to perfect the algorithm's details or parameters

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
2 years ago
Into which of these files would you paste copied information to create an integrated document? A. Mailing list B. Source C. Data
viva [34]

it would be b.mailing list


5 0
3 years ago
Read 2 more answers
A computer never gets tired or bored while working for a long time .. ​
Angelina_Jolie [31]

Answer :Computers never get tired or bored because of doing the same task continuously. They work in the same manner all the time. A computer can store large amount of information in its memory.

Explanation: it is quality which makes the user to be dependent on computer. As we all know, computer systems do repetitive tasks without any boredom, tiredness, or fatigue.

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3 years ago
PLEASE ANSWER FAST!!!! look at the image below
Lubov Fominskaja [6]

Answer:

mbnlkgjhituyhklcjvjhklhur

Explanation:

hjiuyotgjfhkjmrti

7 0
2 years ago
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