Being a subfield under artificial intelligence, machine learning is a vast topic and is one of the most popular fields of study in the world of technology. There are mainly three types of Machine learning algorithms- Supervised learning, unsupervised learning, and Reinforcement algorithms.
The supervised Machine learning algorithm is broadly classified into Regression and Classification Algorithms. In this article, we are going to list and discuss 6 types of classification algorithms. But, first, let us understand what a classification algorithm is.
It is a supervised learning technique of machine learning that is used to determine the categorization of fresh observations based on training data. A software in Classification learns from a given dataset or observations and then classifies additional observations into one of many classes or groupings. The classes are also called targets, labels, or categories. In the classification algorithm, the input data is labeled and a continuous output function (y) is associated with an input variable (x).
Classification algorithms are mainly used to identify the category of any given data set and predict the output for the absolute data. Classification algorithms can be better understood through a real-life application as an example.
Email Spam Detectors are based on machine learning classification algorithms. Binary classifiers are used for this function where the emails received are segregated between ‘Spam’ and ‘Not Spam’.
(Related reading: Binary and multiclass classification)
How to evaluate the classification models?
According to the information provided by javatpoint, the classification models can be evaluated in the following ways:
Log Loss or Cross-Entropy Loss:
Log Loss or Cross-Entropy loss is used to evaluate the output of a classifier, which is a probability value between 0 and 1. The value of log loss for a successful binary Classification model should be close to 0. If the projected value differs from the actual value, the value of log loss rises. The smaller the log loss, the better the model's accuracy. Cross-entropy may be computed for binary classification as follows:
Where x = predicted result, p (x) = probability distribution, q(x) = Estimation
The confusion matrix outputs a matrix/table that describes the model's performance. It is sometimes referred to as the error matrix. The matrix is made up of predicted results in a summary manner, with a total number of accurate and wrong predictions. The matrix is shown in the table below:
Accuracy can be calculated as: (TP + TN) / Total Population
ROC curve is an abbreviation for Receiver Operating Characteristics Curve, while AUC is an abbreviation for Area Under the Curve. It is a graph that depicts the classification model's performance at various thresholds.
The AUC-ROC Curve is used to visualise the performance of the multi-class classification model. TPR and FPR are shown on the ROC curve, with TPR (True Positive Rate) on the Y-axis and FPR (False Positive Rate) on the X-axis.
The types of classification algorithms in machine learning are as follows:
Support Vector Machines
Let us learn about the top six classification algorithms used in machine learning.
(Must read: A Classification and Regression Tree (CART) Algorithm)
6 Classification Algorithms:
Now that we have learned about the different classification models, let us discuss the few popular classification algorithms.
Even though the name has ‘regression’ in it, Logistic Regression is a classification algorithm. It calculates distinct values based on a set of independent variables (s). Simply defined, it forecasts the likelihood of an event occurring by fitting data to a logit function.
As a result, it is also known as logit regression. Because it forecasts the probability, the values obtained will always be between 0 and 1. The advantage of logistic regression is that it is very useful for understanding the significance of many independent variables on a single outcome variable.
(Related blog: What is Regression Analysis? Types and Applications)
But it works only when the predicted variable is binary, assuming that all predictors are independent of one another, and expects that the data is free of missing values.
The python code for logistic regression is:
Support Vector Machines:
The training data is represented as points in space split into categories by a clear separation as broad as feasible in the support vector machine. New instances are then mapped into that same space and projected to belong to one of the categories based on which side of the gap they land on.
Support Vector Machine algorithms are very effective in high-dimensional spaces. It also employs a subset of training points in the decision function, making it memory efficient.
The only disadvantage of SVM is that it does not provide probability estimates directly, instead, they are calculated using costly five-fold cross-validation.
The python code for the support vector machine is:
K-Nearest Neighbors (KNN):
A neighbor-based categorization is a form of lazy learning in that it does not seek to build a general internal model and instead merely saves instances of the training data.
Classification is determined by a simple majority vote of each point's k closest neighbors. This method is easy to develop, resistant to noisy training data, and effective with huge amounts of training data.
Although, the limitation is that the value of K must be determined, and the calculation cost is significant due to the necessity to compute the distance of each instance to all of the training examples.
The thing that one needs to consider before selecting KNN is that it is computationally very expensive. The variables in KNN should be normalized otherwise the higher range variables can bias it.
The Python code for K-Nearest Neighbors is:
(Recommended Read: K-Nearest Neighbor)
The Naive Bayes method is based on Bayes' theorem and assumes independence between every pair of characteristics. Naive Bayes classifiers perform effectively in a wide range of real-world applications, including document categorization and spam filtering.
To estimate the required parameters, this technique takes just a minimal quantity of training data. When compared to more complex approaches, Naive Bayes classifiers are incredibly quick. However, Naive Bayes is known to be a poor estimator.
The python code for the Naive Bayes classifier is:
Given a set of characteristics and their classes, a decision tree generates a set of rules that may be used to categorize the data. Decision Tree is easy to comprehend and visualize, requires minimal data preparation, and can handle both numerical and categorical data.
However, decision trees can build complicated trees that do not generalize well, and decision trees can be unstable since tiny alterations in the data might result in the generation of an entirely new tree.
It can be understood using the diagram given below:
Population is classified into 4 groups based on different attributes to identify if they will play or not (source)
The python code for Decision tree algorithm is:
Random Forest Classifiers:
A random forest classifier is a meta-estimator that fits a number of decision trees on different sub-samples of datasets and utilizes average to enhance prediction accuracy while controlling over-fitting. (learn more about overfitting and underfitting in ML)
The original input sample size is always used as the sub-sample size, but the samples are generated using replacement. In most situations, reducing over-fitting and using a random forest classifier outperforms decision trees. However, real-time prediction is slow, difficult to execute, and the method is complicated.
The python program for the Decision tree algorithm is given below:
Classification algorithms are very important and popular as they have many applications like Email spam detection, speech recognition, identification of cancer tumor cells, drugs classification, biometric identification, and many more.
In this article, we have listed out six popular classification algorithms that are frequently used in machine learning.