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7 most commonly used machine learning models

by Team Techager
7 most commonly used machine learning models

Due to high demand and technological advancements, machine learning has become more popular in recent years. Many industries have found ML appealing because it has the potential to create value out of data. 

In this article, we describe the seven most commonly used machine learning models. They are the mathematical representation of real-world processes. However, if you need a custom ML model, it is best to contact a professional machine learning agency.

Linear Regression

Linear regression attempts to describe the relationship between a dependent variable and one or more independent variables. To examine this relationship, you can use two common tools such as scattered plots or correlation matrices.

Linear regression assumes a linear correlation between the data and the label. Hence, its idea is to find a line that best fits the data and minimizes the discrepancy between predictable values and actual results.

Linear regression models are relatively simple and provide an easy-to-interpret mathematical formula to generate predictions. It has many different uses in both business and science.

Logistic Regression

The logistic regression model is used to predict a finite number of outcomes. Moreover, it assumes that all features are independent of each other.

Basically, logistic regression is used to classify binary data. Therefore, the most important feature of this model is that the independent variable, the so-called dichotomous variable, takes only two values, 0 and 1. Generally speaking, it refers to the occurrence or absence of an event or phenomenon. Logistic regression is used in many areas, e.g., to forecast the previous customer churns, clicks on advertisements, or spam.

Support Vector Machine

Support Vector Machine (SVM) is one of the most powerful ML models, so it couldn’t be omitted from our list. It’s commonly used for regression purposes but is also suitable for classification tasks.

The Support Vector Machine is a boundary that distinguishes two classes with a hyperplane. All data point is represented as a data element in n-dimensional space. Suppose we use two properties to classify different cells, then the decision boundary will be a plane in two-dimensional space. The most important part is determining the location of the decision boundary, which should be as far as possible from the support vectors. Common uses of the SVM algorithm are intrusion detection system, handwriting recognition, protein structure prediction, etc.

Decision Tree

A decision tree is a classification model used in strategic planning, operations research, and machine learning. This model uses a binary tree to decide which label should be assigned to each data point. Decision trees have two main units:

  • The head node where the data is shared,
  • The decision node where we get the final data.

The more nodes a tree has, the more accurate it is. The purpose of this model is primarily to increase the prediction with each partition so that the model continuously gains information about the data set.

Naïve Bayes classifier

Another popular machine learning model is Naïve Bayes. It is used to work with large amounts of data; therefore, it is best suited for real-time forecasting.

This model assumes that the features are independent of each other, which means that changes made to one variable don’t affect others. To understand how this model works, one needs to take a deeper look at Bays’ theorem with conditional probability. The conditional probability is the probability that something will happen, considering that something else has already happened.

Neutral Network

A neural network is a model consisting of neurons that make up layers (input, hidden, and output). This model takes one or more input fields and, by traversing the network of equations, produces one or more output fields. For this model, a large set of learning data is needed to achieve high precision. Therefore, training a good model can take a long time.

The network learns by checking individual records, generating predictions for individual records, and adjusting weights if they cause an incorrect prediction. This process is repeated many times and the network continues to refine its predictions until at least one stopping criterion is met.

Random Forest

Random forests are machine learning models for classification algorithms. It includes several individual decision trees that rely on random characteristics. All decision trees in a random forest are separate models. Each of them uses a subset of random traits to predict a target, and all of these predicted targets stack together to predict a more accurate target.

A literary critic, former writer, literary fan, or librarian will ask a different question to predict who will win the Nobel Prize. They all have different skills, information, and knowledge about the literature, and their methods of achieving the prediction goal will vary. The decision trees of all of these people will create a Random Forest model.

Conclusion: Machine learning models

The details of any particular machine learning model are incredibly complex, but this article should give you an idea of how each one works. If you want to start your first ML project, most likely, you’ll need a help of a professional machine learning agency. They will help you conduct it effectively and avoid many mistakes.

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