Popular algorithms for machine learning consulting services models
1. Decision tree
This is a decision support method based on the use of a tree graph. The decision-making model takes into account their potential consequences, as well as resource consumption and efficiency, calculating the likelihood of an event occurring.
Speaking about building business processes, the tree is formed from the minimum possible number of questions with an unambiguous answer (either “yes” or “no”). Having given the answers, we will come to the right choice.
The problem is structured and systematized, the final decision is made based on logical conclusions.
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2. Naive Bayesian classification
Algorithms of this type belong to the family of simple probabilistic classifiers, which are based on Bayes’ theorem. Independently as independent (this is called a strict or naive assumption).
In machine learning, an algorithm is used to:
- definitions of spam;
- linking news to thematic headings;
- emotional coloring of text material;
- face recognition and other patterns in images.
3. Least squares method
If you’ve studied statistics, you know the concept of linear regression. Least squares is a variant of its implementation. Linear regression allows you to solve problems of fitting a straight line passing through many points.
4. Logistic regression
The way that determines the dependence between variables, if one of them is categorically dependent, independent are independent. It uses a logistic function (accumulative logistic distribution).
Logistic regression is a powerful statistical method for predicting events. It is in demand:
- for credit scoring;
- when you need to build a profit forecast for a specific product;
- assessment of the probability of an earthquake, etc.
An example. We assess the reliability and solvency of borrowers
There is no need to think long about what ML can give here – not only time is saved, but also real money of financial institutions. It is worth remembering Sberbank, which has long fired many employees who were involved in this. Yes, there are more cars, and the automation of this process is common.
In this example of machine learning, the candidates for a loan are objects, and the features are formed from the client’s questionnaire (the features will be different if a legal entity contacts the bank).
Using machine learning, a sample is made that includes “good” credit histories and “bad” ones. As a result, clients are divided into classes, and a decision is made on whether to issue or refuse.
Among the sophisticated machine learning algorithms is the same credit scoring (each client receives points for some features). There is also an algorithm based on precedents.
ML-direction, machines, and neural networks are very popular today, and this popularity is growing, so mastering this direction means guaranteeing your demand in the job market. However, only experts are capable of providing up-to-date knowledge on ML.
