Supervised vs. Unsupervised Machine Learning
The main goal of machine learning algorithms is the discovering patterns in data. Based on the type of questions the research faced and data available, we will choose learning algorithms. They can be divided into two classes according to the way they learn about data. Those are supervised and unsupervised learning.
When we have prior information about the real value of outputs we can create an algorithm using ground truth. Let’s suppose we have input variables (x) and an output variable (Y) and use a learning algorithm to approximate the mapping function from the input to the output:
Y = f(X).
The goal is to find the function f so well that we can predict output variable (Y) when we have new, not training, input data (x). We know the correct answers, and the algorithm iteratively corrects predictions on the training data. It works as a teacher who corrects mistakes and acts as a supervisor giving judging whether you’re getting the right answer. Therefore, this type of algorithm is called supervised learning that means the existence of a labeled data full set in the training process. Fully labeled gives the opportunity to measure prediction accuracy called algorithm performance. Learning stops when the algorithm achieves an acceptable level of performance.
The main difference between the two algorithm types is in using or not a ground truth or prior knowledge while learning is performed. It’s not always easy to have perfectly labeled and clean data sets, sometimes it’s impossible. That’s why the algorithm questions they don’t know the answer are needed. This is the way to unsupervised learning coming in. It doesn’t have labeled outputs so a learning model uses a data set without any instructions on what to do with it. Absence of the corresponding output variables defines the goal in understanding the natural structure of data points. Unsupervised learning is modeling the distribution in the data in order to learn more about the relationship of inputs.
Classification and regression problems are the two main areas where supervised learning is useful. Common algorithms include logistic regression, naive bayes, support vector machines, artificial neural networks, and random forests. The goal of classification algorithms is to predict a categorical or discrete value, identifying the input data as the member of a particular class, or group. On the other hand, the main goal of regression algorithms is to predict the discrete or continues value which doesn’t relate to any class or category. For both classification and regression, the goal is to find the specific function of inputs that allows producing correct output data effectively. The correctness is assumed in terms of training data. It is not to say that outputs are always correct. Incorrect or noisy data labels are the source of incorrectness which reduces the learning effectiveness. Model complexity is another factor affecting algorithm performance. The proper level of complexity depends on the nature of training data. The small amount of data or not uniform distribution throughout different possible scenarios require a low-complexity model. The high-complexity will tend to overfit in those conditions. It means that learning a function will fit training data well but doesn’t generalize to other data points.
Unsupervised learning is referred to such most common tasks as clustering, anomaly detection, representation learning, and density estimation. In all of these cases, the implicit structure of data is the desired goal. The most common algorithms include k-means clustering, principal component analysis, and autoencoders. Since unlabeled data is used, there is no specific way to compare model performance in most unsupervised learning methods. The most widely used unsupervised learning algorithm is clustering. It divides the data points into the number of groups and can give food for thoughts to create an assumption about labels in classification. Anomaly detection is a method for finding rare events in data. It also can be used in feature engineering to filter mistaken observations. Representation learning can find feature associations considered as related. It uses in feature engineering to reduce data dimension and simplify the counting procedures in supervised methods. Autoencoders take input data, compress it into code with the ability to recreate inputs, and can remove noise from data.
Typically, choosing between supervised or unsupervised machine learning algorithms depends on factors defined by the data volume and structure. In reality, both supervised or unsupervised algorithms are used to solve the use case.
