A classification problem is a problem when the dataset has categorical output values. We use classification algorithms in machine learning to make predictions on the such dataset. In this short article, we will go through the list of the top 20 classification algorithms in machine learning that every data analyst and machine learning developer needs to know.
Note that in this article, we will not implement the algorithm in any programming language. This article is going to list the top 20 classification algorithms in machine learning that you can use to train your dataset. You can find the implementation of these algorithms in this table.
List of top 20 classification algorithms in Machine learning
There are many machine learning algorithms that can be used for classification problems. Here is a list of some of the most commonly used algorithms:
- Logistic Regression: Logistic regression is a linear model that is used for binary classification problems. It estimates the probability that an instance belongs to a certain class and then assigns it to the class with the highest probability.
- Decision Trees: Decision trees are a popular choice for classification problems because they are easy to understand and interpret. They work by creating a tree-like model of decisions and splitting the data based on those decisions.
- Random Forests: Random forests are an ensemble learning method that combines multiple decision trees to make more accurate predictions. They work by training multiple decision trees on different subsets of the data and then averaging the predictions made by each tree.
- Support Vector Machines (SVMs): SVMs are powerful and popular algorithmss for classification problems. They work by finding the hyperplane in a high-dimensional space that maximally separates the different classes.
- Naive Bayes: Naive Bayes is a simple and efficient algorithm for classification problems. It is based on the idea of using Bayes’ theorem to make predictions based on the probability of certain events occurring.
- K-Nearest Neighbors (KNN): KNN is a non-parametric classification algorithm that works by finding the K nearest neighbors of an instance and assigning it to the class that is most common among those neighbors.
- Neural Networks: Neural networks are a type of machine learning algorithm that is inspired by the structure and function of the human brain. They can be used for a wide variety of classification tasks and have shown great success in many applications.
- AdaBoost: AdaBoost is a boosting algorithm that is used to improve the accuracy of other classification algorithms. It works by training a series of weak learners and then combining them to create a strong classifier.
- Gradient Boosting: Gradient boosting is another boosting algorithm that is used for classification tasks. It works by training a series of weak learners and adding them together to form a strong classifier.
- Stochastic Gradient Descent (SGD): SGD is an algorithm that is used to train classifiers, including logistic regression and support vector machines. It works by iteratively updating the model’s parameters in the direction that reduces the loss function.
- Linear Discriminant Analysis (LDA): Linear Discriminant Analysis (LDA) is a dimensionality reduction technique that is commonly used for classification tasks. It is a supervised learning method that is used to find the linear combination of features that best separates two or more classes.
- Quadratic Discriminant Analysis (QDA): Quadratic Discriminant Analysis (QDA) is a classification algorithm that is similar to Linear Discriminant Analysis (LDA). Both LDA and QDA are used to find the linear or quadratic combination of features that best separates two or more classes.
- Linear Perceptron: The Perceptron is a linear classification algorithm. This means that it learns a decision boundary that separates two classes using a line (called a hyperplane) in the feature space
- Multilayer Perceptron (MLP): Multilayer Perceptrons are widely used to solve problems requiring supervised learning and research into computational neuroscience and parallel distributed processing. Examples include speech recognition, image recognition, and machine translation.
- Radial Basis Function (RBF) Network: Radial Basis Function (RBF) Networks are a particular type of Artificial Neural Network used for function approximation problems. RBF Networks differ from other neural networks in their three-layer architecture, universal approximation, and faster learning speed.
- Locally Weighted Learning (LWL): Locally Weighted Learning methods are non-parametric and the current prediction is done by local functions. The basic idea behind LWL is that instead of building a global model for the whole function space, for each point of interest a local model is created based on neighboring data of the query point.
- Quadratic Classifier: A quadratic classifier uses a quadratic function to model the relationship between the features and the class labels, and makes predictions based on this model.
- Fisher’s Linear Discriminant: Fisher’s linear discriminant is a classification method that projects high-dimensional data onto a line and performs classification in this one-dimensional space.
- Bayesian Network: A Bayesian network (BN) is a probabilistic graphical model for representing knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the conditional probability for the corresponding random variables.
- Hidden Markov Model (HMM): A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.
Now let us briefly explain each of these algorithms. You can find a full explanation and implementation of some of these algorithms from this list.
Logistic Regression
Logistic regression is a statistical method used for classification tasks. It is a type of supervised learning algorithm that can be used to predict the probability of an outcome.
In logistic regression, the goal is to model the probability of a binary outcome, such as whether a person will default on a loan (outcome of 0) or not (outcome of 1). The model is trained on a dataset that includes a number of input features (also called predictor variables) and the corresponding binary outcome for each example in the dataset.
The logistic regression model uses a logistic function, which is a sigmoid function, to map the input features to a probability between 0 and 1. The model then predicts the probability of the outcome being 1 given the input features.
For example, a logistic regression model could be trained to predict the probability that a customer will purchase a product based on their age, income, and education level. The model would use the input features of age, income, and education to predict the probability of the customer purchasing the product.
Logistic regression is widely used in a variety of applications, such as predicting the probability of a customer churning or the probability of a patient developing a particular disease. It is a simple and effective approach for binary classification problems.
Decision Trees
A decision tree is a flowchart-like tree structure that represents the decisions made and the possible consequences of those decisions. It is a popular method for classifying and predicting outcomes in machine learning and data mining.
A decision tree consists of a root node, branches, and leaf nodes. The root node represents the entire sample population, and the leaf nodes represent the final decision or prediction. The branches represent the decision rules that lead to the final prediction. The decision tree uses a series of binary splits, where the data is split into two parts based on a certain decision rule. The process continues until a leaf node is reached, where the final prediction is made.
Decision trees are simple to understand and interpret, and they can be visualized easily. They are also efficient and fast, making them a popular choice for building machine learning models. However, they can be prone to overfitting, especially when the tree becomes too deep or the data is not diverse enough.
Decision trees are commonly used in applications such as credit risk assessment, medical diagnosis, and sales forecasting. They are also used in a variety of other fields, including finance, marketing, and engineering.
Random forest
Random forest is an ensemble machine learning algorithm that is used for classification and regression tasks. It is a type of bootstrapped ensemble method, which combines the predictions of multiple decision trees to create a more robust and accurate model.
In a random forest, a large number of decision trees are trained on bootstrapped samples of the training data. Each tree in the ensemble is trained on a different sample of the data, and the final prediction is made by averaging the predictions of all the trees. This process helps to reduce the overfitting that can occur when using a single decision tree, and it typically leads to better generalization performance on the test data.
Random forests are simple to use and relatively fast to train, making them a popular choice for many machine-learning applications. They are also very effective at handling large datasets and high-dimensional data, and they are resistant to overfitting. However, they can be less interpretable than a single decision tree, since the final prediction is based on the combined predictions of many trees.
Random forests are used in a wide range of applications, including credit risk assessment, stock market analysis, and healthcare diagnosis. They are also commonly used in natural language processing, image recognition, and computer vision tasks.
Support Vector Machines
Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification or regression tasks. They are based on the idea of finding a hyperplane in a high-dimensional space that maximally separates the different classes.
In the case of classification, an SVM algorithm looks for the hyperplane that maximally separates the classes, while in the case of regression, it tries to find the hyperplane that best fits the data. The SVM algorithm also has a regularization parameter, which helps to control overfitting.
SVMs are particularly effective in cases where the data is not linearly separable, and they have been widely used in various fields, including text classification, image classification, and bioinformatics.
One of the key advantages of SVM algorithms is their ability to handle high-dimensional data efficiently. They are also effective at finding the global optimal solution, thanks to their optimization techniques. However, they can be sensitive to the choice of kernel function and can be computationally expensive to train, especially for large datasets.
Naive Bayes
Naive Bayes is a simple and effective probabilistic machine learning algorithm that is based on the Bayes theorem, which states that the probability of an event occurring is equal to the prior probability of the event multiplied by the likelihood of the event given the evidence.
In the context of machine learning, the Naive Bayes algorithm is often used for classification tasks. It makes predictions based on the probability of an event occurring, given certain features or characteristics of the data.
The “naive” part of the name comes from the assumption that the features are independent of each other, which is often not the case in real-world data. Despite this assumption, the Naive Bayes algorithm has been shown to be very effective in practice, particularly for text classification tasks.
One of the key advantages of the Naive Bayes algorithm is that it is simple and easy to implement, and it is relatively fast and efficient. It is also resistant to overfitting, which makes it a good choice for working with small datasets. However, it can be less accurate than other algorithms when the assumption of independence between features is not valid.
K-Nearest Neighbors
K-Nearest Neighbors (KNN) is a supervised machine learning algorithm that is used for classification and regression tasks. It is based on the idea of identifying the K nearest neighbors of a given data point and using the class or value of those neighbors to make a prediction.
In the case of classification, the KNN algorithm looks for the K data points in the training set that are closest to the new data point and assigns the new point to the class that is most common among those neighbors. In the case of regression, the KNN algorithm calculates the mean or median value of the K nearest neighbors and uses that value as the prediction for the new data point.
The KNN algorithm is simple and easy to implement, and it is relatively fast and efficient. It is also flexible and can be used with different types of data and distance measures. However, it can be sensitive to the choice of K and can be affected by the presence of outliers in the data.
KNN is commonly used in applications such as image classification, speech recognition, and recommendation systems. It is also used in a variety of other fields, including finance, healthcare, and marketing.
Neural Networks
A neural network is a type of machine-learning algorithm that is inspired by the structure and function of the human brain. It is composed of layers of interconnected “neurons,” which process and transmit information.
Neural networks are commonly used for tasks such as image classification, speech recognition, natural language processing, and forecasting. They are particularly effective at learning patterns and relationships in data and can be trained to perform a wide range of tasks.
There are many different types of neural networks, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. The specific architecture of a neural network depends on the task it is being used for and the type of data it is processing.
One of the key advantages of neural networks is their ability to learn and adapt to new data. They are also capable of handling large and complex datasets, and they can learn to recognize patterns and relationships that are not easily apparent to humans. However, they can be computationally intensive to train and may require a large amount of data to achieve good results.
AdaBoost
AdaBoost (Adaptive Boosting) is a boosting algorithm that is used for classification and regression tasks. It is a type of ensemble method, which means that it combines the predictions of multiple weaker models to create a stronger and more accurate model.
AdaBoost works by sequentially training a series of weak models, where each model is trained to correct the mistakes of the previous model. The final prediction is made by combining the predictions of all the weak models using a weighted majority vote. The weights assigned to each model are adjusted based on the model’s performance, with better-performing models receiving higher weights.
AdaBoost is a popular and effective machine learning algorithm that has been used in a variety of applications, including image and video recognition, natural language processing, and anomaly detection. It is relatively simple to implement and can be used with a variety of weak learning algorithms. However, it can be sensitive to noisy data and outliers, and it can be slower to train than some other algorithms.
Gradient Boosting
Gradient Boosting is a machine learning algorithm that is used for classification and regression tasks. It is a type of boosting algorithm, which means that it combines the predictions of multiple weaker models to create a stronger and more accurate model.
In gradient boosting, a series of weak models are trained sequentially, with each model trying to correct the mistakes of the previous model. The final prediction is made by combining the predictions of all the weak models using a weighted majority vote. The weights are adjusted based on the model’s performance, with better-performing models receiving higher weights.
One of the key differences between gradient boosting and other boosting algorithms is the way in which the weak models are trained. In gradient boosting, the weak models are decision trees, and they are trained using an optimization algorithm called gradient descent. This helps to improve the model’s predictive power and make it more robust.
Gradient Boosting is a powerful and effective machine learning algorithm that has been used in a variety of applications, including image and video recognition, natural language processing, and anomaly detection. It is relatively fast and efficient to train, and it is resistant to overfitting. However, it can be sensitive to noisy data and outliers, and it can be harder to interpret than some other algorithms.
Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is an optimization algorithm that is used to minimize the loss function of a model, which is a measure of how well the model fits the training data. It is a popular algorithm for training machine learning models, particularly in the field of deep learning.
In SGD, the model parameters (such as weights and biases) are updated based on the gradient of the loss function with respect to those parameters. The gradient is calculated using the training data, and the model parameters are updated in the direction that minimizes the loss. The process is repeated for a fixed number of iterations or until the loss function converges to a minimum.
SGD is an efficient and effective optimization algorithm that is widely used in machine learning and deep learning. It is simple to implement and can be used with a variety of different types of models. One of the key advantages of SGD is that it is relatively fast and efficient, particularly when working with large datasets. However, it can be sensitive to the choice of learning rate and may not always converge to the global minimum of the loss function.
Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a statistical technique that is used for dimensionality reduction and classification tasks. It is a linear method that is based on the idea of finding a linear combination of features that maximally separates the different classes.
In LDA, the goal is to find the projection of the data onto a lower-dimensional space that maximally separates the different classes. This is done by finding the directions in the feature space that have the highest between-class variance and the lowest within-class variance. The resulting projection can be used for classification by finding the decision boundary that separates the different classes.
LDA is a simple and effective method that is widely used in a variety of applications, including face recognition, speech recognition, and text classification. It is particularly useful for datasets with a large number of features and is relatively efficient and easy to implement. However, it is a linear method and may not be effective for datasets that are not linearly separable.
Quadratic Discriminant Analysis
Quadratic Discriminant Analysis (QDA) is a statistical technique that is used for classification tasks. It is similar to Linear Discriminant Analysis (LDA), but it allows for non-linear decision boundaries between the classes.
In QDA, the goal is to find a projection of the data onto a lower-dimensional space that maximally separates the different classes. This is done by finding the directions in the feature space that have the highest between-class variance and the lowest within-class variance. Unlike LDA, which assumes that the classes have the same covariance matrix, QDA allows for different covariance matrices for each class. This allows for non-linear decision boundaries between the classes.
QDA is a simple and effective method that is commonly used in a variety of applications, including face recognition, speech recognition, and text classification. It is particularly useful for datasets with a large number of features and is relatively efficient and easy to implement. However, it can be sensitive to the presence of outliers in the data and may not always perform as well as LDA when the assumption of different covariance matrices for each class is not valid.
Linear Perceptron
A linear perceptron is a type of artificial neural network that is used for classification tasks. It is a simple model that is composed of a single layer of neurons, and it is trained using a supervised learning algorithm called the perceptron algorithm.
The linear perceptron is a binary classifier, which means that it can only classify data into two classes. It works by finding a linear decision boundary that separates the two classes. The model is trained by adjusting the weights and biases of the neurons in the network, using the perceptron algorithm, until the decision boundary is able to correctly classify the training data.
The linear perceptron is a simple and easy-to-implement model that is often used as a baseline for comparing the performance of more complex models. It is particularly useful for datasets that are linearly separable. However, it is limited by its ability to only classify data into two classes and may not be effective for datasets that are not linearly separable.
multilayer perceptron (MLP)
A multilayer perceptron (MLP) is a type of artificial neural network that is used for classification and regression tasks. It is composed of multiple layers of neurons, and it is trained using a supervised learning algorithm called backpropagation.
The MLP is a feedforward neural network, which means that information flows through the network in one direction, from the input layer to the output layer. The input layer receives the input data, and the output layer produces the prediction. The intermediate layers, called hidden layers, process the information and transmit it to the next layer.
The MLP is a powerful and flexible model that is widely used in a variety of applications, including image and speech recognition, natural language processing, and forecasting. It is particularly effective at learning complex patterns and relationships in data. However, it can be computationally intensive to train, especially for large datasets, and it may require a large number of hidden layers and neurons to achieve good performance.
Radial Basis Function (RBF) Network
A radial basis function (RBF) network is a type of artificial neural network that uses radial basis functions as activation functions. An RBF network is an unsupervised learning algorithm that can be used for clustering, classification, and function approximation.
In an RBF network, the input data is first transformed using a set of basis functions, each of which is centered at a different prototype vector. The output of the network is then a linear combination of these basis functions, with the coefficients of the combination determined by a set of weights.
One advantage of RBF networks is that they can approximate complex, nonlinear functions with a small number of hidden units. They are also relatively easy to train, as the weights can be learned using simple gradient descent methods. However, they can be sensitive to the choice of prototype vectors and may require a large number of hidden units to achieve good performance.
Locally Weighted Learning (LWL)
Locally weighted learning (LWL) is a type of machine learning algorithm that is used to fit a nonlinear function to a set of data points. It is a variant of the popular k-nearest neighbors (k-NN) algorithm, in which the influence of each data point on the function being learned is weighted based on its distance from a query point.
In LWL, a query point is specified and the algorithm finds the k-nearest neighbors to that point in the training data. The weights for each of these neighbors are then computed based on their distance from the query point, with points that are closer to the query point having higher weights. The function being learned is then fit these weighted data points using a linear or nonlinear regression model.
One advantage of LWL is that it can be used to fit functions that are highly nonlinear and vary significantly over different regions of the input space. It is also relatively simple to implement and can be used in a variety of applications, including function approximation, classification, and density estimation. However, it can be computationally expensive, as it requires finding the k-nearest neighbors for each query point.
Quadratic Classifier
A quadratic classifier is a type of machine learning algorithm that can be used for classification tasks, specifically for binary classification problems where the classes are linearly separable. A quadratic classifier works by fitting a quadratic boundary to the data, separating the two classes with a curved boundary rather than a straight line as in a linear classifier.
The quadratic classifier is a special case of the support vector machine (SVM) algorithm, which is a widely used method for classification and regression tasks. In an SVM, the decision boundary is found by maximizing the margin, or the distance between the boundary and the nearest training examples from each class. For a quadratic classifier, this boundary is a quadratic curve rather than a straight line.
Quadratic classifiers can be more effective than linear classifiers in certain situations, particularly when the data is not linearly separable. However, they can be more computationally expensive to train and may require more data to achieve good performance.
Fisher’s Linear Discriminant
Fisher’s linear discriminant is a statistical method used to find a linear combination of features that maximally separates two or more classes of data. It is a type of dimensionality reduction technique that is commonly used in pattern recognition and machine learning applications.
The goal of Fisher’s linear discriminant is to find the linear combination of features that maximizes the ratio of the between-class variance to the within-class variance. The between-class variance measures the spread of the data points from different classes, while the within-class variance measures the spread of the data points within a single class.
Fisher’s linear discriminant can be used for classification tasks, where the linear combination of features is used as a discriminant function to predict the class of a new data point. It is particularly useful in cases where the classes are well-separated and the data is linearly separable. However, it may not perform well if the classes are not well-separated or if the data is nonlinearly separable.
Bayesian Network
A Bayesian network is a graphical model that represents the probabilistic relationships between a set of variables. It is a type of probabilistic graphical model that uses Bayesian inference to make predictions about the probability of events based on the observed data.
In a Bayesian network, the nodes in the graph represent the variables of interest, and the edges between the nodes represent the relationships between the variables. The probability distribution of each variable is represented by a conditional probability table, which specifies the probability of each variable given the values of its parent variables.
Bayesian networks can be used to model complex systems in which the variables are interdependent and the relationships between the variables are probabilistic. They can be used for tasks such as classification, prediction, and causal inference. One advantage of Bayesian networks is that they can handle uncertainty and missing data, and they can be updated as new data becomes available. However, they can be computationally expensive to work with, particularly for large networks with many variables.
Hidden Markov Model (HMM)
A hidden Markov model (HMM) is a type of statistical model that is used to represent a sequence of observations that are generated by a sequence of underlying states. It is a type of temporal probabilistic model that is commonly used in machine learning and natural language processing applications.
In an HMM, the underlying states are hidden, or unobserved, and the observations are the variables that can be observed. The HMM is characterized by the probability distribution of the observations given the underlying states, and the probability distribution of the transitions between the underlying states.
HMMs are used to model time series data, where the underlying states are assumed to be temporally dependent. They are commonly used in tasks such as speech recognition, language modeling, and gene prediction. One advantage of HMMs is that they can handle missing or incomplete data, as the hidden states can be inferred from the observed data. However, they can be computationally expensive to work with, particularly for large datasets.
Summary
In this article, we listed the top 20 classification algorithms in machine learning. Each of these has its own advantages as we discussed some of them in this article.