Types of Machine Learning

There are different ways to train machine learning algorithms, each of which has its own advantages and disadvantages. To understand the pros and cons of each type of machine learning, we must first look at what kind of data they use. In machine learning there are two main categories of data labeled data and unlabeled data. Labeled data has both the input and output values in a completely machine readable pattern. However, it requires a lot of human labor to label the data to begin with. For example, in a churn prediction use case, you will have a bunch of features in columns. And the corresponding target or label column, which implies the state of the customer, which is a churner or a non-churner. Sometimes the human label power required to label this data will be huge and that may slow down the whole process. Unlabeled data, on the other hand do not have any target columns at all. This eliminates the need for human labor, but requires more complex solutions. There are also some types of machine learning algorithms which are used in specific use cases, but three main methods are used today.

In supervised learning, the machine learning algorithm is given a small training dataset to work with. This training dataset is a smaller part of the bigger dataset and serves to give the algorithm a basic idea of the problem, solution and data points to be dealt with. The training dataset is also very similar to the final dataset in its characteristics and provides the algorithm with the labeled parameters required for the problem. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output. This solution is then deployed for use with the final dataset, which it learns from the same way as the training dataset. This means that supervised machine learning algorithms will continue to improve even after being deployed, discovering new patterns and relationships as it trains itself on the new data.

The second type of machine learning is unsupervised learning. Unsupervised machine learning has the great advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine readable, allowing much larger datasets to be used by the machine learning algorithm. In supervised learning, the labels allow the algorithm to find the exact nature of the relationship between any two data points. However, unsupervised learning does not have labels to be used, resulting in the creation of hidden structures. Relationships between data points are perceived by the algorithm in an abstract manner, with no input required from human beings. The creation of these hidden structures is what makes unsupervised learning algorithms versatile. Instead of a defined and set problem statement, unsupervised learning algorithms can adopt to any data by dynamically changing hidden structures. This offers more post deployment development than supervised learning algorithms.

And the third and final type of machine learning is reinforcement learning. Reinforcement learning directly takes inspiration from how human beings learn from data in their daily lives. It features an algorithm that improves upon itself and learns from new situations using a trial and error method. Favorable outputs are encouraged or reinforced and non-favorable outputs are discouraged or punished. Based on the psychological concept of conditioning reinforcement learning works by putting the algorithm in a work environment with an interpreter and a reward system. In every iteration of the algorithm, the output result is given to the interpreter, which decides whether the outcome is favorable or not. In the case of the algorithm finding the correct solution, the interpreter reinforces the solution by providing a reward to the algorithm.

If the outcome is not favorable, the algorithm is forced to reiterate until it finds a better result. In most cases, the reward system is directly tide to the effectiveness of the results. In typical reinforcement learning use cases such as finding the shortest route between two points on a map. The solution is not an absolute value. Instead, it takes on a score of effectiveness expressed in a percentage value. The higher this percentage value is, the more reward is given to the algorithm. Thus, the program is trained to give the best possible solution for the best possible reward.