A Step‑by‑Step Guide to the Standard Machine Learning Workflow

This article presents the standard machine‑learning workflow, noting that its early stages overlap with data‑mining processes such as data acquisition, cleaning, and splitting into training and test sets.

Step 1: Data Acquisition

Collect historical data from logs, databases, or files; completeness and correctness of this data directly affect all subsequent steps.

Step 2: Data Preparation

Clean the collected data, perform preprocessing, label the data, and divide it into training and test sets for later use.

Step 3: Model Selection

Choose an appropriate algorithm based on the task (classification or regression) and the characteristics of the available samples.

Step 4: Learning & Training

Train the model on the training set while iteratively optimizing algorithm parameters.

Step 5: Model Evaluation

After training, compute precision and recall, apply the test set to the model, and assess whether performance meets the required standards.

Step 6: Parameter Optimization

Iteratively adjust model parameters and hyper‑parameters, retrain, and re‑evaluate to continuously improve overall performance.

Step 7: Deployment

Once the model meets criteria, deploy it to production, monitor its behavior, and adjust parameters promptly if errors become excessive, maintaining model reliability.

In practice, data acquisition and preparation are collectively referred to as feature engineering, a highly important and complex phase that typically consumes more than 80% of a project’s effort; many methods exist, which will be detailed in future articles.