Classification algorithms help you divide your data into different classes. Just like when you want to sort things while packing, a classification algorithm helps you in classifying data. In this article, we’ll take a look at what classification algorithms are, the types of classification algorithms, some basic concepts of this topic, and how they work.
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What does Classification mean?
To predict target class, when we use our training dataset to get boundary conditions, we call this process classification. There are many types of target classes you can achieve. For example, suppose you want to predict whether your customers would buy a particular product or not according to the customer data you have. In this case, the target classes would be either ‘Yes’ or ‘No.’
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On the other hand, you might want to classify vegetables according to their weight, size, or color. In this scenario, the available target classes might be Spinach, Tomato, Onion, Potato, and Cabbage. You might perform gender classification as well, where the target classes would be Female and Male.
Let’s understand a little how a classification algorithm works by considering the third example. We can keep the hair length as a feature parameter, although it’s just for the sake of this example. We can train our model by using a classification algorithm and let it determine boundary conditions to perform differentiation between female and male genders through the given feature parameter, i.e., hair length.
Basic Concepts of Classification
Before we begin discussing classification algorithms further, you must be familiar with several definitions. This way, you’d be able to avoid any confusion later on:
It is an individual measurable property of a particular phenomenon we observe at a time.
A classifier is an algorithm that maps the input data of a model to a particular category.
Classification models have to conclude the input values we give to the model during training. These models predict the categories (class labels) for the new data we provide to them.
Multi-label classification is when we map each sample to a set of target labels of multiple classes. For example, a school bag could have books, a lunch box, and pens at the same time.
Multi-class Classification is when we assign every sample to only a single target label. It takes place when we have more than two classes. For example, a car could be moving or stationary, but not both at the same time.
Binary Classification is when we have only two possible classes. For example, a person’s gender could be male or female.
Types of Classification Algorithms
Here are all the types of classification algorithms:
- Kernel Estimation
- Linear Classifiers
(Logistic regression, Fisher’s linear discriminant, and Naive Bayes classifier)
- Quadratic Classifiers
- Neural Networks
- Learning Vector Quantization
- Support Vector Machines
(Least squares support vector machines)
Let’s now discuss some of the essential types of classification algorithms:
K-nearest neighbor, also known as KNN, is a popular algorithm to solve regression and classification problems. It classifies new cases according to the votes of k-neighbors. We determine k-nearest neighbors by using distance functions. The most popular distance function is Euclidean, but there are other options, too, such as Manhattan and Hamming.
To understand KNN, you can take a look at a real-life example. Suppose you want to befriend a person about which you don’t have much information. To get to know them better, you’d first talk to their friends and colleagues to get an idea of what they’re like. This is how the KNN algorithm works.
While using the k-nearest neighbor algorithm, ensure that you normalize the variables as higher range variables can develop a bias. Moreover, KNN algorithms are quite expensive, computationally.
Decision trees help you predict possible outcomes according to a series of choices. It is a supervised learning algorithm and uses various features with continuous and categorical dependent variables.
For example, suppose you want to go out to buy fruits for yourself, but you notice that the weather is cloudy. Now, you have two choices, you might go, or maybe you won’t. If you go, it may rain, and then you’d have to return empty-handed. On the other hand, if it doesn’t rain, you can buy the fruit you need to buy. It was a simple example containing multiple variables, but you get the idea.
Also read: Decision Tree in R
Logistic regression is not a regression algorithm. Logistic regression estimates discrete values according to a particular set of independent variables. In other words, it predicts the chances of an event by using a logit function. That’s why it also has the name of logit regression.
Because logistic regression was designed for Classification, it is a popular choice among experts. Also, it is the most suitable algorithm to understand the influence of various independent variables on a possible result. Its disadvantage is it only works with predictable binary variables and assumes that its data doesn’t contain any missing values.
Support Vector Machine
In a support vector machine, the value of every feature is the value of a specific coordinate, and every item is a point in n-dimensional space. Here, ‘n’ stands for the number of features you have.
Let’s suppose you have two features, hair length, and height. In this case, we’d first plot these variables in a 2-dimensional space, and every point has two coordinates. We call these coordinates Support Vectors; that’s why this algorithm is called Support Vector Machine.
After we plot those points, we’ll find a line that splits the data into two distinctly classified groups. This line is the classifier, and we’d create classes according to the side where our testing data lies in the final result.
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