Name Different Types Of Supervised Machine Learning Algorithms

The scope of this research is primarily on the performance analysis of disease prediction approaches using different variants of supervised machine learning algorithms. Disease prediction and in a broader context, medical informatics, have recently gained significant attention from the data science research community in recent years. Models based on these algorithms use labelled training data of patients for training . For the test set, patients are classified into several groups such as low risk and high risk. To avoid the risk of selection bias, from the literature we extracted those articles that used more than one supervised machine learning algorithm. The same supervised learning algorithm can generate different results across various study settings.

There is a chance that a performance comparison between two supervised learning algorithms can generate imprecise results if they were employed in different studies separately. On the other side, the results of this study could suffer a variable selection bias from individual articles considered in this study. These articles used different variables or measures for disease prediction. We noticed that the authors of these articles did not consider all available variables from the corresponding research datasets. The inclusion of a new variable could improve the accuracy of an underperformed algorithm considered in the underlying study, and vice versa.

Another limitation of this study is that we considered a broader level classification of supervised machine learning algorithms to make a comparison among them for disease prediction. We did not consider any sub-classifications or variants of any of the algorithms considered in this study. For example, we did not make any performance comparison between least-square and sparse SVMs; instead of considering them under the SVM algorithm.

A third limitation of this study is that we did not consider the hyperparameters that were chosen in different articles of this study in comparing multiple supervised machine learning algorithms. It has been argued that the same machine learning algorithm can generate different accuracy results for the same data set with the selection of different values for the underlying hyperparameters . The selection of different kernels for support vector machines can result a variation in accuracy outcomes for the same data set. Similarly, a random forest could generate different results, while splitting a node, with the changes in the number of decision trees within the underlying forest.

Finally, handling missing data using the effective impute package library in Python is discussed. K-nearest neighbor, iterative imputer, and multivariate imputation by chained equations are illustrated with the step-by-step codes in Python. These techniques were applied to impute the missing values in a frac stage data set. The examples and guidelines shown in this chapter can be easily applied to solve various other oil and gas–related problems.

In the previous two machine learning types, there is either labeled or unlabeled data to assist training. Semi-supervised machine learning lies between the two techniques. Data labeling is an expensive and time-consuming process that requires highly-trained human resources. In that regard, there are cases where labels are unavailable in most observations but present in just a handful, and this is where semi-supervised machine learning comes in. Let's take an example of a photo archive that contains both labeled and unlabeled images. Semi-supervised machine learning attempts to solve problems that lie between supervised and unsupervised learning by discovering and learning the structure of the input variables.

Machine learning algorithms that make predictions on a given set of samples. Supervised machine learning algorithm searches for patterns within the value labels assigned to data points. Some popular machine learning algorithms for supervised learning include SVM for classification problems, Linear Regression for regression problems, and Random forest for regression and classification problems. Supervised Learning is when the data set contains annotations with output classes that form the cardinal out classes. In case of sentiment analysis, the output classes are happy, sad, angry etc. A main difference between supervised vs unsupervised learning is the problems the final models are deployed to solve.

Both types of machine learning model learn from training data, but the strengths of each approach lie in different applications. Supervised machine learning will learn the relationship between input and output through labelled training data, so is used to classify new data using these learned patterns or in predicting outputs. Random Forest is the go to machine learning algorithm that uses a bagging approach to create a bunch of decision trees with random subset of the data. The final prediction of the random forest algorithm is derived by polling the results of each decision tree or just by going with a prediction that appears the most times in the decision trees. In this form of machine learning, we allow the algorithm to self-discover the underlying patterns, similarities, equations, and associations in the data without adding any bias from the users' end. Although the end result of these is totally unpredictable and cannot be controlled, Unsupervised Learning finds its place is advanced exploratory data analysis and especially, Cluster Analysis.

Given the growing applicability and effectiveness of supervised machine learning algorithms on predictive disease modelling, the breadth of research still seems progressing. Specifically, we found little research that makes a comprehensive review of published articles employing different supervised learning algorithms for disease prediction. Therefore, this research aims to identify key trends among different types of supervised machine learning algorithms, their performance accuracies and the types of diseases being studied. In addition, the advantages and limitations of different supervised machine learning algorithms are summarised. Machine learning algorithms employ a variety of statistical, probabilistic and optimisation methods to learn from past experience and detect useful patterns from large, unstructured and complex datasets .

Most of these applications have been implemented using supervised variants of the machine learning algorithms rather than unsupervised ones. In the supervised variant, a prediction model is developed by learning a dataset where the label is known and accordingly the outcome of unlabelled examples can be predicted . A labeled data means that some of the data is tagged with the correct output. It is similar to a person learning things from another person. Supervised learning is used for regression and classification to predict a procedure's output. Algorithms in supervised learning learn from the labeled training data, which is beneficial for predicting unpredicted data outcomes.

It takes time to build, scale and deploy accurate machine learning models successfully. Besides that, supervised learning also needs an expert team of skilled data scientists. Artificial neural networks are a set of machine learning algorithms which are inspired by the functioning of the neural networks of human brain. They were first proposed by McCulloch and Pitts and later popularised by the works of Rumelhart et al. in the 1980s .. In the biological brain, neurons are connected to each other through multiple axon junctions forming a graph like architecture. These interconnections can be rewired (e.g., through neuroplasticity) that helps to adapt, process and store information.

Likewise, ANN algorithms can be represented as an interconnected group of nodes. The output of one node goes as input to another node for subsequent processing according to the interconnection. Nodes are normally grouped into a matrix called layer depending on the transformation they perform.

Apart from the input and output layer, there can be one or more hidden layers in an ANN framework. Nodes and edges have weights that enable to adjust signal strengths of communication which can be amplified or weakened through repeated training. Based on the training and subsequent adaption of the matrices, node and edge weights, ANNs can make a prediction for the test data.

Supervised Machine Learning Algorithms Types Figure7 shows an illustration of an ANN with its interconnected group of nodes. The main difference between supervised vs unsupervised learning is the need for labelled training data. Supervised machine learning relies on labelled input and output training data, whereas unsupervised learning processes unlabelled or raw data.

In supervised machine learning the model learns the relationship between the labelled input and output data. Models are finetuned until they can accurately predict the outcomes of unseen data. However, labelled training data will often be resource intensive to create. Unsupervised machine learning on the other hand learns from unlabelled raw training data. An unsupervised model will learn relationships and patterns within this unlabelled dataset, so is often used to discover inherent trends in a given dataset.

During training, engineers give the algorithm data points with an assigned class or category. With classification, an input value is taken and assigned a class or category, depending on the training data provided. For example, judging whether an email is spam or not is an example of classification. If there are more than two classes to choose from, we call it multiclass classification. Common classification algorithms include support vector machines, random forest, decision trees, and k-nearest neighbors. Decision tree is one of the earliest and prominent machine learning algorithms.

A decision tree models the decision logics i.e., tests and corresponds outcomes for classifying data items into a tree-like structure. The nodes of a DT tree normally have multiple levels where the first or top-most node is called the root node. All internal nodes (i.e., nodes having at least one child) represent tests on input variables or attributes. Depending on the test outcome, the classification algorithm branches towards the appropriate child node where the process of test and branching repeats until it reaches the leaf node .

The leaf or terminal nodes correspond to the decision outcomes. DTs have been found easy to interpret and quick to learn, and are a common component to many medical diagnostic protocols . When traversing the tree for the classification of a sample, the outcomes of all tests at each node along the path will provide sufficient information to conjecture about its class. An illustration of an DT with its elements and rules is depicted in Fig.3.

Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias , and an output. If that output value exceeds a given threshold, it "fires" or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.

When the cost function is at or near zero, we can be confident in the model's accuracy to yield the correct answer. Supervised machine learning is used to classify unseen data into established categories and forecast trends and future change as a predictive model. A model developed through supervised machine learning will learn to recognise objects and the features that classify them. Predictive models are also often trained with supervised machine learning techniques. By learning patterns between input and output data, supervised machine learning models can predict outcomes from new and unseen data.

This could be in forecasting changes in house prices or customer purchase trends. If supervised learning uses labeled input and output data, an unsupervised learning algorithm works on its own to discover the structure of unlabeled data. Unsupervised learning comes in handy when the human expert has no idea what to look for in the data. Unlike supervised learning, it is best suited for more complex tasks, including descriptive modeling and pattern detection. The chief difference between unsupervised and supervised learning is in how the algorithm learns. In unsupervised learning, the algorithm is given unlabeled data as a training set.

Unlike in supervised learning, there are no correct output values; the algorithm determines the patterns and similarities within the data, as opposed to relating it to some external measurement. In other words, algorithms are able to function freely in order to learn more about the data and find interesting or unexpected findings that human beings weren't looking for. Unsupervised learning is popular in applications of clustering and association .

Supervised learning models can be a valuable solution for eliminating manual classification work and for making future predictions based on labeled data. However, formatting your machine learning algorithms requires human knowledge and expertise to avoid overfitting data models. Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.

Unsupervised learning is another form of machine learning which uses machine learning algorithms to analyze and cluster unlabeled datasets for analysis. We can utilize and harness powerful tools like Python and R to implement various types of machine learning algorithms to make the most out of the data. Apart from that, we can also integrate these models into various end-user applications.

The final dataset contained 48 articles, each of which implemented more than one variant of supervised machine learning algorithms for a single disease prediction. All implemented variants were already discussed in the methods section as well as the more frequently used performance measures. Based on these, we reviewed the finally selected 48 articles in terms of the methods used, performance measures as well as the disease they targeted. It found 55 articles that used more than one supervised machine learning algorithm for the prediction of different diseases. Out of the remaining 281 articles, only 155 used one of the seven supervised machine learning algorithms considered in this study.

The rest 126 used either other machine learning algorithms (e.g., unsupervised or semi-supervised) or data mining methods other than machine learning ones. ANN was found most frequently (30.32%) in the 155 articles, followed by the Naïve Bayes (19.35%). Semi-supervised learning falls somewhere between the supervised and unsupervised machine learning techniques by incorporating elements of both methods. This method is used when there is only a limited set of data available to train the system, and as a result, the system is only partially trained.

The information the machine generates during this partial training is called pseudo data and later on computer combines both labeled and the pseudo-data to make predictions. However, if there are complex interactions among features, then algorithms such as decision trees and neural networks work better, because they are specifically designed to discover these interactions. Linear methods can also be applied, but the engineer must manually specify the interactions when using them.

If the feature vectors include features of many different kinds , some algorithms are easier to apply than others. Methods that employ a distance function, such as nearest neighbor methods and support-vector machines with Gaussian kernels, are particularly sensitive to this. An advantage of decision trees is that they easily handle heterogeneous data.

Unsupervised machine learning and supervised machine learning are frequently discussed together. Unlike supervised learning, unsupervised learning uses unlabeled data. From that data, it discovers patterns that help solve for clustering or association problems.

This is particularly useful when subject matter experts are unsure of common properties within a data set. Common clustering algorithms are hierarchical, k-means, and Gaussian mixture models. Classification uses an algorithm to accurately assign test data into specific categories. It recognizes specific entities within the dataset and attempts to draw some conclusions on how those entities should be labeled or defined. Common classification algorithms are linear classifiers, support vector machines , decision trees, k-nearest neighbor, and random forest, which are described in more detail below. Here, we know the value of input data, but the output and the mapping function both are unknown.