In conversations about artificial intelligence (AI) and machine learning (ML), the terms ML model and ML algorithm are often used interchangeably. However, they refer to different concepts in the context of machine learning.
ML Algorithm
An ML algorithm is a method or a procedure used to train a model from data. It defines the steps and mathematical operations needed to learn the model's parameters based on the input data. Algorithms include the specific techniques and processes that are applied to the data to create a model.
Examples of ML algorithms include:
- Linear Regression Algorithm: A method to model the relationship between a dependent variable and one or more independent variables using a linear equation.
- Decision Tree Algorithm: A method that splits the data into branches to make predictions based on certain conditions.
- Support Vector Machine (SVM) Algorithm: A method that finds the hyperplane that best separates different classes in the feature space.
- Neural Network Algorithms: Methods used to train neural networks through backpropagation and gradient descent.
ML Model
An ML model is the result of training an algorithm on data. It represents the learned patterns, relationships, and insights from the data, which can then be used to make predictions or decisions. The model consists of the specific parameters and structure learned by the algorithm.
Examples of ML models include:
- Trained Linear Regression Model: The specific linear equation with learned coefficients that can predict the dependent variable based on new input data.
- Trained Decision Tree Model: The specific tree structure with branches and leaves that can classify or predict outcomes based on new input data.
- Trained SVM Model: The specific hyperplane and support vectors that can classify new data points.
- Trained Neural Network Model: The specific architecture and weights of the neural network that can make predictions on new data.
Key Differences
1. Nature:
- Algorithm: The process or set of rules to be followed in calculations or other problem-solving operations.
- Model: The end product resulting from the application of an algorithm to a dataset.
2. Function:
- Algorithm: Provides the steps needed to train a model.
- Model: Contains the learned parameters and structure that can be used for making predictions.
3. Example Process:
- Algorithm: Gradient Descent, which is used to minimize the error in a neural network.
- Model: The actual neural network with its learned weights after training.
4. Dynamic vs. Static:
- Algorithm: Typically involves a dynamic process during training.
- Model: Usually a static artifact used during inference after training is complete.
Analogy: Think of an algorithm as a recipe and the model as the final dish. The recipe (algorithm) guides you on how to combine and cook ingredients (data) to create the dish (model). The final dish (model) is what you serve, and it can be evaluated for its taste (performance) and used to decide if any modifications are needed in the recipe (algorithm) for better future dishes.
** While models can be considered final or static when compared to algorithms, in fact they must be monitored as part of a continuous cycle (think of CI/CD and DevOps) and adjusted to maintain accuracy. This is because changes in data patterns can affect their accuracy.