Colab notebooks are usually slow and meant for showing the basic usage of a repo. The fact that it is not evaluating during training could be just as simple as they don’t consider it necessary having that step in a simple notebook. The more complex examples like training with perioding evaluation are in their repo. … Read more
What Are Face Datasets? Image datasets include digital images chosen especially to help test, train, and evaluate the performance of ML and artificial intelligence (AI) algorithms, typically computer vision algorithms. Specifically, face datasets include images of human faces, curated for machine learning (ML) projects. See a list of commonly used face datasets. A face dataset … Read more
I have a panel dataset that looks something like: df <- data.frame(id = c(1:10), income_3 = c(2:11), income_4 = c(3:8, NA, 10:12), income_5 = c(4:13), health_3 = c(NA, 6:14), health_4 = c(7:10, NA, 11:15), health_5 = c(9:18)) id income_3 income_4 income_5 health_3 health_4 health_5 1 2 3 4 NA 7 9 2 3 4 5 … Read more
I trained an xgb model like this: candidates_var_train <- model.matrix(job_change ~ 0 + ., data = candidates_train) candidates_train_xgb <- xgb.DMatrix(data = candidates_var_train, label = ifelse(candidates_train$job_change == “Interested”, 1, 0)) candidates_var_test <- model.matrix(job_change ~ 0 + ., data = candidates_test) candidates_test_xgb <- xgb.DMatrix(data = candidates_var_test, label = ifelse(candidates_test$job_change == “Interested”, 1, 0)) Got a decent AUC … Read more