# Machine Learning : Workflow

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This post gives a brief introduction to a workflow of machine learning model and mostly used R packages before diving into the details. **K & L Fintech Modeling**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Given a problem to be solved, all machine learning (ML) models use the same input but different output. It is, therefore, useful to understand a common workflow of ML model. As there is no only one workflow but a variety of it, we also introduce one of them.

### Sample Splitting

Construction of ML model starts from a sample splitting. Most commonly used technique is a K-fold cross validation with random shuffling. In case of time-series or panel data, the K-fold cross validation without random shuffling is used for preserving temporal sequence (

**future data can not be used as a predictor of past data**). This method is called as K-fold forward chaining cross validation or forward chaining shortly. Two cross validations are illustrated in the following figures.

### Workflow of Machine Learning

Although there are many alternatives for each step, most ML models have the following workflow in common.

### Hyperparameters and R packages

R provides many ML packages which are updated irregularly. We use representative time-tested and mostly used R packages for

**selected**some ML models in the following way.

Here, names of selected ML models include

**Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), Gradient Boosting (GBoost) and Extreme Gradient Boosting (XGBoost)**. Numerical values for hyperparameters of each ML model are presented as examples and are not absolute.

### Concluding Remarks

Based on this workflow of ML model, we are going to investigate each ML model and implement it by using R ML packages step by step in a series of next posts. \(\blacksquare\)

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