In machine learning, we use different techniques such as regression, clustering, and classification to analyze datasets to produce insights that can help businesses make informed decisions. In this article, we will discuss classification in machine learning. We will also discuss the basic intuition of different classification algorithms in machine learning.
What Is Classification in Machine Learning?
Classification is a supervised machine learning algorithm that we use to classify data points.
For classification, we use an existing labeled dataset. In the dataset, each data point has a specific class label. Using the data points and their class labels, we train classification models using different algorithms. The model then learns the relationship between the attributes of the data points and their class labels. Once trained, we can use the models to classify new and unseen data points.
A simple example of classification is email spam filtering. In spam detection, we train a machine-learning model on a dataset of labeled emails. Here, the labels indicate whether an email is a spam or not. The model learns to identify patterns and features in the emails that are indicative of spam, such as certain keywords or sender addresses. Once trained, We can use the model to classify new incoming emails as spam or not spam.
Different Classification Algorithms in Machine Learning
Based on the available dataset, we use different types of classification algorithms in order to classify data. Broadly, we can group the classification models into two groups – linear classification models and non-linear classification models.
- Linear classification algorithms are based on the idea that the relationship between the input features and the output class can be represented by a linear function. Some of the linear classification algorithms are as follows.
- Logistic regression
- Support Vector Machines
- Linear Discriminant Analysis (LDA)
- Non-linear classification algorithms are machine learning models that can learn complex, non-linear decision boundaries between classes. These algorithms are able to capture more complex relationships between inputs and outputs than linear classification algorithms. Some of the Non-linear classification algorithms include the following.
- K-Nearest Neighbor (KNN) Algorithm
- Kernel Support Vector Machines
- Naive Bayes
- Decision trees
- Random forest
- Multilayer Perceptron
In the next sections, we will discuss the basic intuition of the above classification algorithms.
Linear Classification Algorithms in Machine Learning
Linear classification algorithms are supervised machine learning algorithms that separate a set of data points into different categories. They do so by finding a linear boundary between the data points. Linear classification algorithms use a linear function to make predictions based on the features in the input dataset and are often used for binary classification problems (i.e. classifying data into two categories).
We use the logistic regression algorithm to find the probability that an input sample belongs to a particular class, rather than simply returning a class label as in traditional linear regression. For this, we use a logistic function (also called the sigmoid function) to model the probability, which produces output values between 0 and 1.
The logistic regression algorithm finds the best coefficients (also called weights) for the input features in order to separate the data into the different classes with the highest accuracy. The coefficients are chosen in such a way that it maximizes the likelihood of the observed data or minimizes the negative log-likelihood, which is a common loss function for logistic regression.
Support Vector Machines
Support Vector Machines (SVMs) are another type of linear classification algorithm. Like logistic regression, they are used to separate data points into different categories by finding a linear boundary between them. However, SVMs have a slightly different approach to solving this problem.
The main idea behind SVMs is to find the hyperplane (an n-1 dimensional subspace) that maximally separates the different classes in the n-dimensional feature space. The hyperplane is chosen in such a way that it maximizes the margin, which is the distance between the hyperplane and the closest data points from each class (also known as support vectors). These closest data points are the ones that are most difficult to classify correctly. So, by maximizing the margin, the SVM aims to find a decision boundary that is least likely to misclassify new data.
Linear Discriminant Analysis (LDA)
LDA is a dimensionality reduction technique used to project the input data into a lower-dimensional feature space that captures the most relevant information for machine learning algorithms.
You can also use Linear Discriminant Analysis as a linear classification algorithm to find a linear combination of features that separates the different classes in the data with the highest accuracy. LDA is a supervised algorithm and it assumes that the data from each class is normally distributed and that the covariance matrices of the different classes are identical.
Using LDA, you can find the linear combination of features (also called discriminant functions) that maximizes the ratio of the between-class variance to the within-class variance. The between-class variance measures the spread of the data across the different classes. On the other hand, the within-class variance measures the spread of the data within each class. By maximizing the ratio, you can find a linear boundary that separates the classes with the highest accuracy.
Perceptrons are simple linear classification algorithms that developed in the late 1950s. It is a type of artificial neural network that consists of a single layer of artificial neurons, also known as perceptrons. The perceptron algorithm is used to classify input data into one of two classes, by finding the best linear boundary between the data points.
The perceptron takes a set of input features as input. For each input, it computes a weighted sum of the features, which is then passed through a step function (also called an activation function) to produce the output. The step function outputs a value of 1 or -1, depending on whether the input is classified as belonging to class 1 or class 2.
The perceptron algorithm starts with an initial set of weights, and it adjusts the weights iteratively based on the misclassifications of the training data. The algorithm stops when it finds the best set of weights that separates the training data with the highest accuracy.
Non-Linear Classification Models in Machine learning
Non-linear classification algorithms are a type of supervised machine learning algorithm that separate a set of data points into different categories by finding a non-linear boundary between them. These algorithms are used when the data is not linearly separable. We can use non-linear classification algorithms to model more complex and realistic patterns in the data.
K-Nearest Neighbors (KNN) is a non-parametric, instance-based classification algorithm. It is called non-parametric because it does not make any assumptions about the underlying distribution of the data. We call it instance-based because it does not learn a model from the training data, but instead stores the training examples and makes predictions based on the stored instances.
Given a new data point, the KNN classification algorithm finds the k-nearest neighbors in the training set based on a distance metric (such as Euclidean distance). The majority vote of the class labels of the k-nearest neighbors is used as the prediction for the new data point.
It’s also worth mentioning that the k-NN algorithm has a variant called weighted k-NN, where each neighbor has a different weight based on its distance from the test sample. This way, the closer neighbors have more influence in the final decision.
Kernel Support Vector Machines
Support Vector Machines (SVMs) are linear classification algorithms, but they can be extended to solve non-linear classification problems by using the kernel trick. The kernel trick transforms the input data into a higher-dimensional feature space, where a linear boundary can be found to separate the different classes.
To transform the input data, we use a kernel function. It takes the form of a dot product between the original input features. Common kernel functions include the polynomial kernel, the radial basis function (RBF) kernel, and the sigmoid kernel.
- The polynomial kernel computes the dot product between the input features raised to a certain power (p).
- The radial basis function (RBF) kernel function computes the dot product between the input features and a radial basis function, which is a function that depends on the Euclidean distance between the input features.
- The sigmoid kernel function computes the dot product between the input features and a sigmoid function, which is a function that produces values between 0 and 1.
The kernel trick allows SVMs to model complex and non-linear decision boundaries. It makes SVMs a powerful and flexible algorithm for solving non-linear classification problems.
It’s important to note that the choice of kernel function depends on the specific characteristics of the data and the problem being solved. We often select the kernel function using trial and error to produce the best results.
Naive Bayes is a probabilistic classification algorithm. It is based on Bayes’ theorem and the assumption of independence between the features. Bayes’ theorem states that the probability of a hypothesis (H) given some evidence (E) is proportional to the probability of the evidence given the hypothesis (P(E|H)) multiplied by the prior probability of the hypothesis (P(H)). In the context of classification, the hypothesis is the class label, and the evidence is the input features.
The Naive Bayes algorithm makes the assumption that the features are conditionally independent given the class label, which means that the probability of one feature is not affected by the value of the other features.
The naive Bayes algorithm is simple, fast, and efficient. It’s widely used in various applications such as text classification, spam filtering, and sentiment analysis. However, the independence assumption is often unrealistic, and it can lead to poor performance in certain cases.
The Decision Trees are a popular non-linear classification algorithm that uses a tree-like model of decisions and their possible consequences. Decision trees can handle both continuous and categorical features, and they are easy to interpret and understand.
The algorithm works by recursively splitting the data into subsets based on the feature that provides the highest information gain. Each split forms a new decision node, and the final leaves of the tree represent the class labels. The decision tree algorithm starts with the entire dataset. It recursively splits the dataset into subsets based on the feature that provides the highest information gain.
The decision tree algorithm is simple and easy to interpret, but it can be prone to overfitting, especially if the tree is grown to its maximum depth. There are various techniques to prevent overfitting such as pruning, setting the minimum number of samples in a leaf, or using an ensemble method such as random forests.
Suggested Reading: Where to Find the Best Machine Learning Datasets
Random Forest is an ensemble method that combines multiple decision trees to improve the accuracy and robustness of the classification model. It creates multiple decision trees by bootstrapping the data (randomly sampling the data with replacement) and selecting a random subset of features at each split. The final output is the majority vote of the predictions made by all the decision trees.
The main idea behind Random Forest is to reduce the variance of a single decision tree by averaging the predictions of multiple decision trees, which are trained on different subsets of the data and use different subsets of features. This averaging process reduces the overfitting problem that is often encountered in decision trees.
One of the main advantages of the Random forest algorithm is that it is less prone to overfitting than a single decision tree. It averages the predictions of multiple decision trees, which are trained on different subsets of the data and use different subsets of features. Random Forest also has the ability to handle both categorical and continuous features, and it’s robust to outliers.
A Multilayer Perceptron (MLP) is a type of artificial neural network that can be used for classification in machine learning. It is composed of multiple layers of artificial neurons.
In a multilayer perceptron,
- The input layer receives the feature inputs.
- One or more hidden layers process the data.
- The output layer provides the final classification output.
The network is trained using a supervised learning algorithm, such as backpropagation, to adjust the weights of the connections between the neurons. This allows the network to learn the relationships between the input features and the output classes, and make predictions for new input data. MLP can be used for binary or multi-class classification problems.
In this article, we have discussed the basics of classification in machine learning. We also discussed the different types of classification algorithms along with their intuition and functioning.
I hope you enjoyed reading this article. To read more about machine learning, you can read this article on clustering in machine learning. You might also want to read this article on regression in machine learning.