Classification is the process of predicting the class of given data points. Classes are sometimes called targets, labels or categories. Classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to discrete output variables (y.)
What Is Classification in Machine Learning?
Classification is a supervised machine learning process that involves predicting the class of given data points. Those classes can be targets, labels or categories. For example, a spam detection machine learning algorithm would aim to classify emails as either “spam” or “not spam.” Common classification algorithms include: K-nearest neighbor, decision trees, naive bayes and artificial neural networks.
For example, spam detection in email service providers can be identified as a classification problem. This is a binary classification since there are only two classes marked as “spam” and “not spam.” A classifier utilizes some training data to understand how given input variables relate to the class. In this case, known spam and non-spam emails have to be used as the training data. When the classifier is trained accurately, it can be used to detect an unknown email.
Classification belongs to the category of supervised learning where the targets are also provided with the input data. Classification can be applied to a wide-variety of tasks, including credit approval, medical diagnosis and target marketing, etc.
Types of Classification in Machine Learning
There are two types of learners in classification — lazy learners and eager learners.
1. Lazy Learners
Lazy learners store the training data and wait until testing data appears. When it does, classification is conducted based on the most related stored training data. Compared to eager learners, lazy learners spend less training time but more time in predicting.
Examples: K-nearest neighbor and case-based reasoning.
2. Eager Learners
Eager learners construct a classification model based on the given training data before receiving data for classification. It must be able to commit to a single hypothesis that covers the entire instance space. Because of this, eager learners take a long time for training and less time for predicting.
Examples: Decision tree, naive Bayes and artificial neural networks.
More on Machine Learning: Top 10 Machine Learning Algorithms Every Beginner Should Know
There are a lot of classification algorithms to choose from. Picking the right one depends on the application and nature of the available data set. For example, if the classes are linearly separable, linear classifiers like logistic regression and Fisher’s linear discriminant can outperform sophisticated models and vice versa.
Important Classification Algorithms to Know
- Decision tree
- Naive Bayes
- Artificial neural network
- K-nearest neighbor (KNN)
A decision tree builds classification or regression models in the form of a tree structure. It utilizes an “if-then” rule set that is mutually exclusive and exhaustive for classification. The rules are learned sequentially using the training data one at a time. Each time a rule is learned, the tuples covered by the rules are removed. This process continues until it meets a termination condition.
The tree is constructed in a top-down, recursive, divide-and-conquer manner. All attributes should be categorical. Otherwise, they should be discretized in advance. Attributes in the top of the tree have more impact in the classification, and they are identified using the information gain concept.
A decision tree can be easily over-fitted generating too many branches and may reflect anomalies due to noise or outliers. An over-fitted model results in very poor performances on the unseen data, even though it gives off an impressive performance on training data. You can avoid this with pre-pruning, which halts tree construction early, or through post-pruning, which removes branches from the fully grown tree.
Naive Bayes is a probabilistic classifier inspired by the Bayes theorem under the assumption that attributes are conditionally independent.
The classification is conducted by deriving the maximum posterior, which is the maximal
P(Ci|X), with the above assumption applying to Bayes theorem. This assumption greatly reduces the computational cost by only counting the class distribution. Even though the assumption isn’t valid in most cases since the attributes are dependent, surprisingly, naive Bayes is able to perform impressively.
Naive Bayes is a simple algorithm to implement and can yield good results in most cases. It can be easily scaled to larger data sets since it takes linear time, rather than the expensive iterative approximation that other types of classifiers use.
Naive Bayes can suffer from a problem called the zero probability problem. When the conditional probability is zero for a particular attribute, it fails to give a valid prediction. This needs to be fixed explicitly using a Laplacian estimator.
Artificial Neural Networks
An artificial neural network is a set of connected input/output units, where each connection has a weight associated with it. A team of psychologists and neurobiologists founded it as a way to develop and test computational analogs of neurons. During the learning phase, the network learns by adjusting the weights so as to be able to predict the correct class label of the input tuples.
There are several network architectures available today, including feed-forward, convolutional and recurrent networks. The appropriate architecture depends on the application of the model. For most cases, feed-forward models give reasonably accurate results, but convolutional networks perform better for image processing.
There can be multiple hidden layers in the model depending on the complexity of the function that the model is going to map. These hidden layers will allow you to model complex relationships, such as deep neural networks.
However, when there are many hidden layers, it takes a lot of time to train and adjust the weights. The other disadvantage of this is the poor interpretability of the model compared to others like decision trees. This is due to the unknown symbolic meaning behind the learned weights.
But artificial neural networks have performed impressively in most real world applications. It has a high tolerance for noisy data and is able to classify untrained patterns. Usually, artificial neural networks perform better with continuous-valued inputs and outputs.
All of the above algorithms are eager learners since they train a model in advance to generalize the training data and use it for prediction later.
K-Nearest Neighbor (KNN)
K-Nearest Neighbor is a lazy learning algorithm that stores all instances corresponding to training data points in n-dimensional space. When an unknown discrete data is received, it analyzes the closest k number of instances saved (nearest neighbors) and returns the most common class as the prediction. For real-valued data, it returns the mean of k nearest neighbors.
In the distance-weighted nearest neighbor algorithm, it weighs the contribution of each of the k neighbors according to their distance using the following query, giving greater weight to the closest neighbors:
Usually, KNN is robust to noisy data since it is averaging the k-nearest neighbors.
How to Evaluate a Classifier
After training the model, the most important part is to evaluate the classifier to verify its applicability.
Machine Learning Classifier Evaluation Methods
- Holdout method.
- Precision and recall.
- Receiver operating characteristics (ROC) curve.
More on Machine Learning: How Does Backpropagation in a Neural Network Work?
There are several methods to evaluate a classifier, but the most common way is the holdout method. In it, the given data set is divided into two partitions, test and train. Twenty percent of the data is used as a test and 80 percent is used to train. The train set will be used to train the model, and the unseen test data will be used to test its predictive power.
Overfitting is a common problem in machine learning and it occurs in most models. K-fold cross-validation can be conducted to verify that the model is not overfitted. In this method, the data set is randomly partitioned into k-mutually exclusive subsets, each approximately equal in size. One is kept for testing while others are used for training. This process is iterated throughout the whole k folds.
Precision and Recall
Precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of relevant instances that have been retrieved over the total amount of relevant instances. Precision and recall are used as a measurement of the relevance.
Receiver Operating Characteristics (ROC) Curve
A ROC curve provides a visual comparison of classification models, showing the trade-off between the true positive rate and the false positive rate.
The area under the ROC curve is a measure of the accuracy of the model. When a model is closer to the diagonal, it is less accurate. A model with perfect accuracy will have an area of 1.0.
In machine learning, classification is a predictive modeling problem where the class label is anticipated for a specific example of input data. For example, in determining handwriting characters, identifying spam, and so on, the classification requires training data with a large number of datasets of input and output.What are the steps of classification in machine learning? ›
- Collecting Data: As you know, machines initially learn from the data that you give them. ...
- Preparing the Data: After you have your data, you have to prepare it. ...
- Choosing a Model: ...
- Training the Model: ...
- Evaluating the Model: ...
- Parameter Tuning: ...
- Making Predictions.
Machine learning (ML) classification problems are those which require the given data set to be classified in two or more categories. For example, whether a person is suffering from a disease X (answer in Yes or No) can be termed as a classification problem.How do you solve classification problems? ›
In Deep Learning, classification problems are solved by training classification models. The classification models are trained by providing objects and their labels. The models learn and identify similar features of objects in a class. After training, the model is tested on a separate data it was trained.What are the 3 main types of data classification? ›
Data classification generally includes three categories: Confidential, Internal, and Public data.What are the 7 stages of classification? ›
There are seven major levels of classification: Kingdom, Phylum, Class, Order, Family, Genus, and Species.What are the 4 objectives of classification? ›
Five objectives of classification are:- (i)The creation of a method for quickly recognising a species, whether it is known or unknown. (ii)The description of various species. (iii)Recognition of different species. (iv)To distribute qualities at different levels of a hierarchy.What are the 5 types of classification? ›
What are the different levels of classification? The organisms are classified according to the following different levels- Kingdom, Phylum, Class, Order, Family, Genus and Species.What are the two types of classification in machine learning? ›
Different Types of Classification Tasks in Machine Learning. There are four main classification tasks in Machine learning: binary, multi-class, multi-label, and imbalanced classifications.Which algorithm is used for classification in machine learning? ›
- Logistic Regression.
- Naive Bayes.
- K-Nearest Neighbors.
- Decision Tree.
- Support Vector Machines.
Naive Bayes classifier algorithm gives the best type of results as desired compared to other algorithms like classification algorithms like Logistic Regression, Tree-Based Algorithms, Support Vector Machines. Hence it is preferred in applications like spam filters and sentiment analysis that involves text.What are the two methods of classification? ›
Common classification methods can be divided into two broad categories: supervised classification and unsupervised classification.Which method is used for classification? ›
There are many techniques for solving classification problems: classification trees, logistic regression, discriminant analysis, neural networks, boosted trees, random forests, deep learning methods, nearest neighbors, support vector machines, etc, (e.g. see the R package “e1071” for more example methods).How can classification models improve accuracy? ›
- Method 1: Acquire more data. ...
- Method 2: Missing value treatment. ...
- Method 3: Outlier treatment. ...
- Method 4: Feature engineering. ...
- Method 1: Hyperparameter tuning. ...
- Method 2: Applying different models. ...
- Method 3: Ensembling methods. ...
- Method 4: Cross-validation.
What are the 8 levels of the Linnaean classification system? From lowest to highest, the present taxonomic system contains eight degrees of hierarchy: species, genus, family, order, class, phylum, kingdom, and domain.What is the basics of classification? ›
-The system of classification helps scientists in studying certain groups of organisms. -The living things are classified into seven different levels, these are kingdom, phylum, classes, order, families, genus, and species. -Kingdoms: These are the most basic classification of living things.What is the purpose of classification? ›
The purpose of classification is to break a subject into smaller, more manageable, more specific parts. Smaller subcategories help us make sense of the world, and the way in which these subcategories are created also helps us make sense of the world. A classification essay is organized by its subcategories.What is a simple example of data classification? ›
Data Classification Examples
Credit card numbers (PCI) or other financial account numbers, customer personal data, FISMA protected information, privileged credentials for IT systems, protected health information (HIPAA), Social Security numbers, intellectual property, employee records.
Classification algorithms are used to categorize data into a class or category. It can be performed on both structured or unstructured data. Classification can be of three types: binary classification, multiclass classification, multilabel classification.What are the 4 main characteristics of classification? ›
Typically, there are four classifications for data: public, internal-only, confidential, and restricted.What are the four important bases of classification criteria? ›
- Qualitative Base.
- Quantitative Base.
- Geographical Base.
- Chronological Base.
- Exhaustability. The classification should be made in an exhaustive manner so that each and every item of the data must belong to any one of the classes without leaving any item to be shown under any class viz. ...
- Exclusiveness. ...
- Homogeneity. ...
- Consistency. ...
- Flexibility. ...
(i) It helps in studying wide variety of living organisms. (ii) It provides a clear picture of all life forms before us. (iii) It helps in understanding the inter-relationship among different groups of organisms. (iv) It provides a base for the development of other biological sciences.What are the 6 groups of classification? ›
The levels of classification, from broadest to most specific, include: kingdom, phylum, class, order, family, genus, and species.What is example of classification *? ›
If you have a group of things, such as fruits or geometric shapes, you can classify them based on the property that they possess. For example, you can classify the apples in one category, the bananas in another, and so on. Similarly, geometric shapes can be classified as triangles, quadrilaterals, and so on.What is meant by classification of data? ›
Data classification is broadly defined as the process of organizing data by relevant categories so that it may be used and protected more efficiently. On a basic level, the classification process makes data easier to locate and retrieve.Is classification supervised or unsupervised? ›
The Classification algorithm uses labeled input data because it is a supervised learning technique and comprises input and output information.What is difference between classification and regression? ›
The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc. and Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.What is the difference between classification and clustering? ›
They appear to be a similar process as the basic difference is minute. In the case of Classification, there are predefined labels assigned to each input instance according to their properties whereas in clustering those labels are missing.
Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on.What is difference between classification and machine learning? ›
Regression vs Classification in Machine Learning: Understanding the Difference. The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels.What is difference between clustering and classification? ›
Classification is a supervised learning approach where a specific label is provided to the machine to classify new observations. Here the machine needs proper testing and training for the label verification. Clustering is an unsupervised learning approach where grouping is done on similarities basis.