This code demonstrates the use of many weak multi-layer perceptron neural networks in a random net to better classify images and avoid overfitting. The commonly known random forest algorithm develops many weak decision trees; however, this example shows how a data scientist can take the same approach by replacing decision trees with neural networks. Instead of saying ‘random forest’, one might call it a ‘random net’.

Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. The use of two functions, hierarchy and hierarchyStats, is demonstrated below. Convert standard employee and supervisor relationship data into useful formats for robust analytics, summary statistics, and span of control metrics. Install the package from CRAN by running the install.packages("hR") command.

This example demonstrates the use of a custom F1 metric in a LightGBM implementation for binary classification with imbalanced data.

This example, written in R, shows you how to train an XGBoost model to predict flower species using the famous Iris data set. XGBoost is known to regularly outperform other algorithms for classification, and it a powerful algorithm for most data scientists.