Which image file format is the best format to use for photographs on the Web
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Answer:
Please check the attachment.
Explanation:
The adjacency matrix is the matrix that has nodes as rows and columns. The nodes if connected is stated as 1 or else 0. And the adjacency list representation is the list with nodes and connected nodes. The nodes that are not connected are not being listed. The diagram and list as well as matrix can be found in the attachment.
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.
