A Tour of Machine Learning Algorithms
There is no machine learning algorithm that has been the best at solving every problem, especially when it comes to predictive modeling. For instance, you cannot claim that neural networks work better than decision trees, or the other way around. There is a number of factors to be considered, like the size of your data set and how it is structured. Therefore, when facing a problem, numerous machine learning algorithms can be used to tackle it and to then later carry out a hold-out test set to identify which algorithm performs better for that particular problem.
Remember that the algorithms you choose must be relevant to your problem, which is where choosing the correct machine learning task comes into play. Machine learning can be one of the best ways to improve the performance of your business, but you might need a machine learning engineer to help you out with the complex machine learning areas.
Machine learning algorithms are categorized in two ways:
1. Algorithms By The Learning Style
There are various ways an algorithm can tackle a problem, which will also depend on the input data. Most books that deal with machine learning or artificial intelligence usually consider some of the learning styles that machine learning algorithms can adapt. Classifying machine learning algorithms is important as it helps you determine the role input data plays, which in turn helps you pick the correct algorithm relevant to your problem. Some of the learning styles in machine learning algorithms are:
This is also called training data, and usually, the model has a known result at a particular moment in time. It is prepared using a training process that is then used to make predictions, but the model can be corrected if it turns out that the predictions are wrong. The training process will go on and on until the moment the desired accuracy level has been obtained. Some of the problems dealt with here include classification and regression. The algorithms used include logistic regression and neural networks.
Here the input data is not labeled and the result is unknown. The model is prepared by logically concluding the structures available in the input data. This can be achieved by either using a mathematical approach, which brings down redundancy cases or by organizing data that is similar. Some of the problems dealt with here include clustering and association rule learning. The algorithms used are K-means and Apriori learning.
2. Algorithms Grouped By Similarity
Grouping of algorithms based on similarity depends on how they work. The grouping method is important, but sometimes there are algorithms that fall into multiple categories. Let’s take a look at some of the algorithms in this category:
Regression has been used to identify the relationship between various variables with the help of error measurement in the predictions made by the model. Regression methods have been regularly used to analyze statistical data, and they have also been used in statistical machine learning. Regression can also be used to refer to the class of the problem and at the same time to the class of the algorithm. Some of the common regression algorithms include linear regression, logistic regression, and ordinary least square regression.