When handling voluminous data that is highly sensitive, it is always preferable to group it into categories or classes. That is primarily where classification algorithms make themselves useful. Classification algorithms are one of the most widely implemented classes of supervised machine learning algorithms.
Classification involves the use of machine learning algorithms to assign a label to samples from a problem domain or training dataset. A real-world example of the classification task is the assigning of ‘spam’ and ‘not spam’ labels to the appropriate e-mail.
Let us first discuss what typically defines a classification task and what its types are. Then, we will go on to enumerate the top five most widely implemented classification algorithms.
What are Classification Tasks and What are their Types?
Classification algorithms are those that involve supervised learning techniques involving labels. These techniques are used to classify searches from a domain of training data into different categories or classes. By learning this type of categorization, a machine learning-based program learns to properly classify each new observation from a given dataset.
The following are the types of classification tasks based on the labels assigned:
Binary classification tasks involve classes defining two fundamental states, normal and abnormal. The class for the normal state is assigned the class label 0 and the abnormal state class is assigned the label 1. For instance, in spam detection, ‘not spam’ is the normal state and ‘spam’ is the abnormal state.
Multi-class classification strays away from the concept of normal and abnormal states. Samples are classified based on how they fit into one among a range of identified classes. Some problems like face recognition and plant species classification result in the number of classes being very large.
These are classification tasks where there are several class labels and one or more class labels may have to be predicted for each element in a dataset. Predicting the presence of multiple objects in a photo with labels such as ‘bicycle’, ‘lamppost’, etc. is how multi-label classification is implemented.
In imbalanced classification tasks, the number of examples in each class is unequally distributed. Usually, these tasks are binary classification tasks where there is a majority of normal class examples and a minority of abnormal class examples in the training dataset. By undersampling the majority class or oversampling the minority class, the composition of the sample dataset is changed.
Top 5 Machine Learning Classification Algorithms
All classification type algorithms in machine learning are used for predictive modeling problems where a class label needs to be predicted for a given example of input data. This technology was used by Daffodil’s ML Team for partial detection of product mentions and prediction of categories for video auto-tagging for an online beauty retailer.
These algorithms can be applied for both structured and unstructured datasets. Some of the most widely used classification algorithms are as follows:
1)Logistic Regression Algorithm
This algorithm involves calculations to predict a binary outcome; either something produces a particular result or it does not. In simple terms, outcomes could be Yes/No, In/Out, or Spam/Clear. Logistic regression is one of the techniques that machine learning derived from the field of statistics.
Without in-depth knowledge of linear algebra or statistics, a machine learning programmer can apply this algorithm for predictive modeling. At the core of this algorithm, the logistic function is applied called the sigmoid function.
The sigmoid function is used by statisticians to describe the properties of population growth. This function uses maximum-likelihood estimation to make assumptions about the distribution of data.
2)Naive Bayes Algorithm
Naive Bayes is a probabilistic machine learning algorithm that is based on the Bayes Theorem. Because of its probabilistic capability, the algorithm can be coded up easily and the predictions can be made quickly in real-time.
Most real-world applications tend to work best with this algorithm because of its scalability. Software processes, where the user’s requests need to be addressed instantaneously, can reap the benefits of this algorithm.
The Bayes Rule applied for this algorithm’s implementation makes use of the concept of conditional probability. Say, the training dataset takes the input X and returns Y as a response. For each row of the dataset, we compute the probability of Y given that X is an event that already happened.
3)K-Nearest Neighbor Algorithm
The K-Nearest Neighbor (KNN) algorithm works on the principle of finding the closest relatives in a training dataset. It classifies the data points based on the class of the majority data points amongst the k neighbors. Here k refers to the number of neighbors to be considered.
The KNN algorithm uses some basic mathematical distance formulae such as Euclidean distance, Manhattan distance, etc. These formulae are used to define some degrees of similarity of distance or proximity among the k-nearest data points.
After applying various values of k, that value is chosen which helps reduce the number of errors in unseen data. KNN is the easiest algorithm to implement. It does not require setting multiple parameters or making additional assumptions like the other algorithms.
When the algorithm implements the training and classification of datasets using Support Vector Machines (SVM), very complex predictions can be made. Data is classified within varying degrees of polarity.
This supervised machine learning algorithm can be used to resolve the challenges associated with both classification and regression. If we were to assign two features x and y to our data, the algorithm would have to plot the data across (x,y) coordinates. The occurrence of the desired features across a graph is then plotted.
SVM finds its implementation in emotional analysis in systems designed to gauge and boost employee performance. The more complex the data, the more accurate the SVM predictor will become through learning.
5)Decision Tree Algorithm
The decision tree algorithm is able to order classes in the dataset on a precise level. Extending from the ‘tree trunk’ to the ‘branches’ of the tree-like structure of the ordered classes, data points are separated into two similar categories.
As you go along the length of the decision tree, the categories become more finitely similar. Classification becomes more organic requiring little to no human supervision, creating categories within categories.
This algorithm copies human-level thinking making for some reliable intuition and interpretations of data. Decision trees do not require prior normalization of data. The only drawback is that any small change done in the data can lead to a large change in its structure.
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You can improve the capabilities of your software solutions with cutting-edge machine learning that makes use of classification algorithms. It makes functionalities like identification of issues and stakeholders, report metrics, and integrations with other technologies seamless.
The application of the appropriate machine learning technique can ease the challenges of scalability and continuous improvements. You can learn further about how Daffodil can help you leverage machine learning models to interpret data and identify complex patterns.