# Top 7 examples of supervised learning algorithms

Supervised learning algorithms are great for solving problems with a large amount of training data. The supervised learning algorithms are great for classifying high-dimensional data representing high-dimensional vectors and matrices. This post will discuss seven examples of supervised learning algorithms.

**Linear Regression**

It is a supervised learning algorithm that relates the value of one or more independent variables to the value of a dependent variable. The goal is to find linear combinations of these independent variables that can also predict values for your dependent variable.

The process behind linear regression is simple: you have some data, which might be either a set of samples or entire population distribution.

You then choose one or more continuous variables and their corresponding values and use them as inputs into a linear equation whose coefficients represent how much each input contributes to predicting your outcome variable's value.

**Decision Trees**

Decision trees are based on the principle that if you have enough examples of your training data set, then you can use these examples to create one tree per decision variable in your problem. In this case, multiple branches would come out of each node, representing different possible outcomes or predictions made by our model using each input variable.

**Support Vector Machines (SVM)**

Support vector machines (SVM) are supervised learning algorithms in binary classification. The SVM is also known as a kernel-based classifier. It uses the concept of high-dimensional data points to determine which of two classes (or categories) will be most beneficial for further analysis and prediction.

**Logistic Regression**

It is a supervised learning algorithm that can use for classification, binary classification, and multi-class classification. Given its probability density function, it predicts the probability of an event occurring.

**Nearest Neighbor**

Nearest neighbor is a supervised learning algorithm used to classify data.

The algorithm uses information about each point in your datasets, such as its x and y coordinates and color or shape, to determine how similar each point is to itself (its Euclidean distance). The value of each feature used by this algorithm will vary depending on what you're trying to do with your data set.

**Gaussian Naive Bayes**

The Naive Bayes model is a generative model. (Gaussian) Naive Bayes assumes that each class has a Gaussian distribution. The basic idea behind GNB is that we have a set of training data (a bunch of examples), and we want to predict what event will happen next in our new example.

**Random Forest**

Random Forest is a supervised learning technique that uses multiple decision trees to make predictions. It gets used in many fields, including biology and machine learning.

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