Which type of data is generally stored in different file formats, such as text files, spreadsheets, and so on?

Computers and Technology

2 answers:

seraphim [82]2 years ago

5 0

Answer:

Plain Text

Explanation:

The simplest way to store information in a computer is plain text. Plain text formate can use lowest common storage formats. The basic concept of plain text is that data are arranged in a row with different values.

Two type of Plain text

1)Delimited formats:

2)Fixed-width formats:

A file is a block of computer memory that interpreting the bytes in a file. For example, a plain text format means that each byte is used to represent a single character.

For example:

The Comma-Separated Value (CSV) format is a type of a plain text format.
Common feature of the plain text is line ending

zepelin [54]2 years ago

3 0

Answer:

Plain text storage (eg, CSV, TSV files)

Sequence Files.

Avro.

Parquet.

You might be interested in

What is causing the electricity prices to increase in South Africa?

Pepsi [2]

The law of demand says that the higher the price of a good is, the less people will demand it. But South Africa , sales of electricity have been dropping, yet the utility wants consumers to even pay more for its good. The national utility, South Africa wants consumers to pay more for electricity.

4 0

2 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.