Designing a learning system is the crucial first step toward implementing machine learning algorithms effectively. A well-designed learning system lays the foundation for accurate predictions, efficient data processing, and improved decision-making.
In this article, we aim to guide you through the essential considerations and best practices involved in creating a robust learning system.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
The above definition is one of the most well-known definitions of Machine Learning given by Tom Mitchell. For any learning problem, we must know the factors T (Task), P (Performance Measure), and E (Training Experience). Let's take a few examples to understand these factors.
Understanding T (Task), P (Performance Measure), and E (Training Experience) With Examples
Problem 1: Handwriting recognition learning problem
For handwriting recognition learning problems, TPE would be,
Task T: To recognize and classify handwritten words within the given images.
Performance measure P: Total percent of words being correctly classified by the program.
Training experience E: A set of handwritten words with given classifications/labels.
Problem 2: Spam Mail detection learning problem
For a system being designed to detect spam emails, TPE would be,
Task T: To recognize and classify emails into 'spam' or 'not spam'.
Performance measure P: Total percent of emails being correctly classified as 'spam' (or 'not spam' ) by the program.
Training experience E: A set of emails with given labels ('spam' / 'not spam').
Problem 3: Checkers learning problem
For a checkers learning problem, TPE would be,
Task T: To play checkers
Performance measure P: Total percent of the game won in the tournament.
Training experience E: A set of games played against itself
If we are able to find the factors T, P, and E of a learning problem, we will be able to decide the following three key components:
-
The exact type of knowledge to be learned (Choosing the Target Function)
-
A representation for this target knowledge (Choosing a model for the Target Function)
-
A learning mechanism (Choosing an approximation algorithm for the Target Function)
Steps for Designing a Learning System
Choose Target Function
Let's take the example of a checkers-playing program that can generate the legal moves (M) from any board state (B). The program needs only to learn how to choose the best move from among these legal moves. Let's assume a function NextMove such that:
NextMove: B -> M
Here, B denotes the set of board states, and M denotes the set of legal moves given a board state. NextMove is our target function.
Choose the Representation of the Target Function
We need to choose a representation that the learning algorithm will use to describe the function NextMove. The function NextMove will be calculated as a linear combination of the following board features:
-
xl: the number of black pieces on the board
-
x2: the number of red pieces on the board
-
x3: the number of black kings on the board
-
x4: the number of red kings on the board
-
x5: the number of black pieces threatened by red (i.e., which can be captured on red's next turn)
-
x6: the number of red pieces threatened by black
NextMove = u0 + u1x1 + u2x2 + u3x3 + u4x4 + u5x5 + u6x6
Here u0, u1 up to u6 the coefficients will be chosen(learned) by the learning algorithm.
Choose a Function Approximation Algorithm
To learn the target function NextMove, we require a set of training examples, each describing a specific board state b and the training value (Correct Move ) y for b. The training algorithm learns/approximates the coefficients u0, u1 up to u6 with the help of these training examples by estimating and adjusting these weights.
We will explore the different ways to find the coefficient u0, u1 up to u6 in the next blog. In the meanwhile, think of any learning problem and try to find out a suitable Target function Representation for that. How about a chess game?
Conclusion
To recapitulate, creating a learning system is an important first step in applying machine learning methods. It entails a thorough examination of the issue domain, the selection of suitable algorithms, data collection and preparation, and model performance assessment. It is possible to build accurate and reliable predictive models that can provide useful insights into a variety of areas by following a methodical strategy to create a learning system.
As machine learning evolves, staying current on the newest techniques and algorithms is critical for developing effective learning systems. Overall, creating a learning system is an iterative process requiring meticulous attention to detail and a dedication to continuous development.
Frequently Asked Questions(FAQs)
1. What is the initial stage in creating a learning system?
The first stage is to comprehend the issue area and then choose the best algorithm for the job.
2. What role do data gathering and preparation play in the creation of a learning system?
Data gathering and preparation are critical components in ensuring that the learning system has access to pertinent and precise data in order to make accurate predictions.
3. What variables should be considered when choosing a program for a learning system?
The sort and nature of the data, accessible computational tools, and success measures are all factors to consider.
4. Why is it essential to evaluate the success of a learning system?
Evaluating success enables us to find areas where the model can be enhanced and assists us in making decisions.
You may also like:
