Отправлено: 12.11.24 12:50. Заголовок: What is ML?

click hereMachine Learning (ML) systems are composed of several key components that work together to build models capable of learning from data and making predictions or decisions. Here's an overview of the main components:

1. Data:
Training Data: The historical data used to train the model, containing input features and corresponding outputs (labels or targets). The quality and quantity of data are crucial for model performance.
Test Data: A separate portion of data used to evaluate the model after training. It helps assess how well the model generalizes to new, unseen data.
Validation Data: Sometimes, an additional set of data is used to fine-tune the model and prevent overfitting during the training process.
2. Features (Input Variables):
Features are the measurable properties or characteristics of the data that are fed into the model. Examples of features could include age, income, and education level for predicting credit scores.
Feature Engineering: The process of transforming raw data into features that improve the model’s performance (e.g., normalizing data, creating interaction variables).
3. Model:
A model is a mathematical representation of the relationship between input data (features) and the output (target). The model learns from training data to make predictions or decisions.
Types of Models: Depending on the task and learning approach, models can range from simple linear regression to more complex neural networks.