Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization

The article introduces a practical, end‑to‑end workflow for using machine learning to solve real‑world industrial problems, emphasizing that supervised learning is the most common approach in production environments.

Machine learning is defined as a scientific discipline that builds algorithms capable of learning from data. The typical offline training pipeline includes data selection and cleaning, feature extraction, model training and model optimization, while the online inference pipeline applies the trained model to new data.

Reasons for adopting machine learning include the abundance of big‑data, cheap high‑performance computing, inexpensive large‑scale storage, and the presence of high‑value problems that justify the investment.

To illustrate the workflow, the article uses a DEAL transaction‑amount forecasting problem. It discusses the choice between a single model and a multi‑model approach, presenting a comparison table of advantages and disadvantages, and recommends a multi‑model strategy for this case.

Data preparation stresses the importance of consistent data distribution between training, testing and online environments, low label noise, and cautious sampling. Common issues such as distribution shift, noisy labels, and biased sampling are described along with mitigation tactics.

Feature extraction distinguishes high‑level (generic) and low‑level (specific) features, showing how they affect coverage and dimensionality. Examples of feature engineering are provided, followed by a discussion of feature normalization methods (rescaling, standardization, unit‑length scaling) with illustrative images.

Feature selection techniques—filter, wrapper, and embedded methods—are outlined to reduce dimensionality and avoid over‑fitting.

Model training uses Logistic Regression as a concrete example. It defines the hypothesis function, loss function (maximum likelihood), and presents the mathematical formulation. Optimization algorithms covered include Gradient Descent (batch, stochastic), Newton’s method (and quasi‑Newton variants such as BFGS, L‑BFGS, OWLQN), and Coordinate Descent, each accompanied by schematic diagrams.

训练数据少，High Level特征多，则使用“复杂”的非线性模型（流行的GBDT、Random Forest等）；
训练数据很大量，Low Level特征多，则使用“简单”的线性模型（流行的LR、Linear-SVM等）。

Model optimization addresses situations where the trained model underperforms. It explains underfitting vs. overfitting, provides a diagnostic table comparing training and test set performance, and lists concrete remedies such as adjusting regularization, changing model complexity, adding data, or performing feature selection.

In conclusion, successful machine‑learning solutions require clear business understanding, reliable data, thoughtful feature engineering, appropriate model selection, and systematic debugging of under‑/over‑fitting issues.