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crimeas [40]
3 years ago
12

What visual practice provides more information about logical relations when creating an ERD

Computers and Technology
1 answer:
Marina86 [1]3 years ago
5 0

Answer:

Logical Data Model

Explanation:

An Entity Relationship Diagram (ERD) is a model or a diagram designed to reveal the relationship between various entities such as people, places, objects, and so on within an institution. Entity relationship diagram is also known as Entity relationship model. This model is used to design relational database by various sectors such as education, engineering, business, etc.

<u>ERD is designed using conceptual data model, logical data model, and physical data model</u>.

The visual practice or model that gives more insight into the logical relations among the entity is the logical data model.

The logical data model is used to illustrate more detailed operational and relational data among the entities. It is more detailed than the conceptual data.

So, the correct answer is logical data model.

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This functions for providing the quick accessing towards the records through database.The database files contain the data in the ESE indexes and is common to computer information and the user.

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You do not really know that you have a hand. Here is why. Imagine the possibility in which you are only a handless brain floatin
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Answer:

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2 years ago
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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.

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3 years ago
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