Answer:
Searching and sorting.
Explanation:
A data dictionary can be defined as a centralized collection of information on a specific data such as attributes, names, fields and definitions that are being used in a computer database system.
In a data dictionary, data elements are combined into records, which are meaningful combinations of data elements that are included in data flows or retained in data stores. This ultimately implies that, a data dictionary found in a computer database system typically contains the records about all the data elements (objects) such as data relationships with other elements, ownership, type, size, primary keys etc. This records are stored and communicated to other data when required or needed.
Basically, when a database management system (DBMS) receives data update requests from application programs, it simply instructs the operating system installed on a server to provide the requested data or informations.
Binary search is an efficient algorithm used to find an item from a sorted list of items by using the run-time complexity of Ο(log n), where n is total number of elements.
Binary search applies the principles of divide and conquer.
A primary key refers to a keyword found in a relational database which uniquely identifies each record contained therein.
A relational database can be defined as a type of database that is structured in a manner that there exists a relationship between its elements. Also, in a relational database, a single space within a row or column contains exactly one value.
Hence, the function of primary keys is that, primary keys facilitates searching and sorting because they act as unique identifiers.
Answer:
i need this for a challenge
Explanation:
Answer:
False
Explanation:
Website designers design how the site looks not works
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.
CCTV, ICTV, IPTV, ITV. These are all surveillance types. :))
