Training a machine learning model is the process of teaching an algorithm to recognize patterns in data and make accurate predictions. The training phase involves feeding the model with labeled datasets, adjusting its internal parameters, and optimizing its performance through multiple iterations.
The core steps of model training include:
Data Collection – Gathering and preprocessing data for training.
Feature Selection – Identifying the most relevant input features.
Model Selection – Choosing an appropriate machine learning algorithm.
Training the Model – Adjusting model parameters through optimization.
Evaluation and Fine-Tuning – Assessing performance and refining the model.
Successful model training requires high-quality data, well-defined objectives, and effective optimization techniques. The ultimate goal is to develop a model that generalizes well to unseen data, avoiding overfitting or underfitting.
