Machine learning algorithms are comparable to students in school in the sense that each one learns differently. Depending on the nature of the data available and the desired outcome, there is a variety of approaches to train these algorithms.
However, how can you identify the best way to train your neural network? The secret lies in understanding what the diverse training methods entail.
Supervised Machine Learning
Supervised learning mostly applies to straightforward tasks that involve labeled data sets. In such cases, the correct answer (output) to every question (input) is already known. Therefore, a data scientist will show the computer the correct input/output pairs. The model already knows the correct answer the algorithm should produce before it starts working. As such, all it needs to do is establish a method of working its way from the input to the output.
A good example of this involves handwriting recognition. The training process involves showing a machine a series of images of handwritten data, together with the proper labels for each digit. With this input, the computer will learn the patterns connecting every image to its label. Essentially, the model predicts the correct answer by comparing any input to available training examples.
Using such explicit illustrations for training a model is not only straightforward in terms of implementation but also easy to comprehend. However, it is only possible to use this approach if one has access to a dataset of reference points.
A majority of the machine learning systems being used globally prefer this training approach. Supervised learning is generally applicable in solving two main types of problems:
In the most basic sense, classification entails using an algorithm to predict a discrete value. It categorizes the input data to a particular group or class. For example, using a training dataset of flower varieties, images might be pre-labeled as roses, lilies or lilacs. A trainer might then assess the algorithm based on its accuracy in classifying other images in those three categories.
On the other hand, regression involves predicting or estimating a continuous quantity. For instance, the model can be used to determine the possible future value of a company’s stocks. In order to calculate this, the model would require past examples of correct input/output pairs related to the problem at hand. These could include historical prices on specific dates and their value after a specified timeframe. With this information, the machine would try to assess the functional relationships between the details provided and the value of stocks.
Unsupervised Machine Learning
It is not always possible to collect clean labeled datasets. And at times, data scientists use algorithms to find answers to questions that they themselves cannot answer. Unsupervised learning comes in handy in such situations.
Basically, it seeks to establish patterns for problems whose solutions are either unobservable or otherwise difficult to obtain. In some cases, there is no right answer to begin with. All a researcher needs to do is give the model an unlabeled data set without any explicit instructions. The model will then attempt to find structure in the data through analysis and extraction of useful features. In the simplest terms, the machine goes into this form of learning completely blind, without any reference data.
It could take a number of approaches to accomplish this:
A model might identify objects that share some similarities, group them together and assign them labels based on their features.
Under this approach, a model essentially correlates features of a data sample with other features. By assessing major attributes of a set of data, it predicts other related attributes. To illustrate, for a customer who picks out a baby monitor and diapers, such system can recommend a pacifier and a bib.
- Detecting anomalies
One of the most popular applications of unsupervised algorithms is in fraud detection. They detect and flag fraudulent activity by taking note of unusual patterns. The same concept applies when it comes to flagging outliers in a given dataset.
Autoencoding involves compressing input data into a code and then recreating a version of the original based on the summarized code. While this option has few practical applications, it is commonly used to remove background noise from visual data so as to enhance picture quality.
It is easy to see why unsupervised learning is not yet as popular as supervised learning. But it holds tons of benefits with regard to the future of machine learning. First, it does not require as much human intervention than the previous method. The model automatically finds correlations and common attributes, in effect, teaching itself using available data.
Second, it offers a dynamic solution which changes with the times. Supervised learning relies on specific rules, and if the rules change, one might need to retrain their model from scratch. However, an unsupervised algorithm will make sense of changing datasets, retaining relevance even in dynamic fields.
In research fields where getting labeled data is difficult or infeasible, this approach can offer incredible results. Since it uses a high degree of probability, it has high chances of inaccuracies. But its advantage lies in the ability to recognize such inaccuracies, learn from them and improve estimation capacity.
Reinforcement Machine Learning
Reinforcement learning, which is a close cousin of unsupervised learning, involves giving machines free rein to determine the best course of action in a given situation. But in order to ensure that the system grows, it uses a reward mechanism to reinforce good performance.
To understand this concept, consider what happens in a video game. Players subconsciously learn from cues to improve performance over time. They earn a reward based on the number of moves they use to defeat an opponent. Every time they complete a level, they earn some sort of badge. On the contrary, stepping into a trap means game over. With time and practice, they learn the best way to achieve their objective and earn the most rewards.
Similarly, this form of machine learning involves establishing an optimal way to achieve a given objective. Every time the model does something right, they get a reward. This sort of feedback enables the machine to identify the best and most rewarding strategy to accomplish a task.
Since this is a trial-and-error method, the models may fail a number of times before getting it right. But as they keep trying and getting different outcomes, they get feedback telling them whether they are right or wrong. Eventually, they figure out a winning strategy that optimizes the reward.
Reinforcement learning holds massive potential as it allows machine learning models to find its own way to achieve objectives. This approach is most common in situations where the solutions to a given problem are infinite or extremely large. For example, in a game of chess, a player can use countless moves to achieve their goal. But not every move holds the potential to give them a win in the long run. A machine in this case will learn the best possible move based on the reward it gets from that move. By learning from experience, it can draw on its growing knowledge base to determine future moves.
Supervised, Unsupervised and Reinforcement Machine Learning at a Glance
|Labeled dataset||Unlabeled dataset||Learning through experience|
|Uses input/output pairs to determine the best approach for solving a problem||Attempts to make sense of data by analyzing features and patterns||Trial-and-error approach that quantifies performance using a reward system|
Which solution is the best?
As it turns out, there is no correct or incorrect way to train a machine learning algorithm. It all depends on the tools and resources you have at hand, particularly the kind of data available. The different available approaches vary in complexity and are suitable for varying situations.
Supervised machine learning works best in situations where labeled data is available and a researcher knows the correct output of a given input. But in situations where data is unlabeled and there is no correct answer to a problem, unsupervised machine learning would be better. On the other hand, reinforcement machine learning works well when the solution space is vast and a machine can learn from experience through trial-and-error. In certain cases, the best training approach involves a hybrid of two or all of the above systems.