Algorithms and artificial intelligence are hot topics within the world of data science, as data science professionals are able to use these tools in order to automate the process of completing a data science project. By definition, algorithms are a set of commands that are given to a machine in order to teach it how to perform a certain task or to learn about a specific topic or subject. Similar to algorithms, artificial intelligence (AI) focuses on teaching machines how to do tasks that are usually completed by humans. Data scientists use algorithms and artificial intelligence by employing machine learning models in order to complete some of their more difficult or mundane tasks. Whether you need to automate the process of data collection, clean a large dataset, or even create prototypes of a model, there is a multitude of machine learning algorithms that can be employed to make the process of completing data science projects much easier and more efficient.
Data Science Algorithms for Machine Learning
Within the realm of data science, algorithms make up the foundation of automated systems and machine learning models through their inclusion in multiple platforms and projects. Most algorithms are based on statistical analysis and mathematical theories which allow these models to be represented by graphs and other data visualizations. The following list includes some of the top algorithms for data science and machine learning.
1. Linear Regression
This model of statistical analysis is generally used to make predictions based on the understanding that there is a relationship between an independent and dependent variable. The equation for linear regression is “y = bo + b1x”, in which the independent variable (x) can be identified as a quantifiable predictor while the dependent variable (y) returns a quantifiable outcome. Then the b coefficients are used to predict the strength of the relationship between X and Y. By inputting different variables into the equation data scientists can measure the effects that one variable has on other variables, such as the effect of a predictor variable (like X=BMI), on an outcome variable (such as making predictions about other markers of health).
There are also several types of regressions available within data science tools and programs, and regression with more than two variables involves the use of multiple (usually correlated) quantitative predictor variables and one quantitative outcome variable. By combining these variables in the equation, the data analyst decides on an order of entry for the predictor variables based on some theoretical rationale. Only then can a series of regression equations be estimated. When working with regression models it is common to use statistical software, such as SPSS and Stata, which offer several options for analyzing and visualizing data with this algorithm.
2. Logistic Regression
Simplifying the process of linear regression, logistic regression is used in situations when there are only two potential outcomes of the model. In data science, logistic regression is used when the decisions are based on one thing or the other, such as health test data that needs to determine whether someone is positive or negative for an illness, or data from a scholastic test that is being graded based on a Pass or Fail. Within data and software engineering, this algorithm can also be used to determine what something or someone is or is not. For example, the creation of CAPTCHA tests which determine whether or not a user is a robot based on their interpretation of an image, words, or number sequences.
When working with logistic regression in a data science tool, the equation for the “Sigmoid” (logistic function) is “y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))” is commonly used. Similar to the equation for linear regression, this equation allows users to unpack the relationship between outcome variable (y) and predictor variable (x) as well as the b coefficients. However, this algorithm differs in form with the addition of the variable “e” which is the natural logarithm and a constant that is used within the statistical analysis and mathematical functions. This equation returns a binary (0,1) or two decision model when utilized within data science programs.
3. Decision Trees
This type of algorithm is primarily used for creating classifications and predictions based on one central piece of information or data. The appearance of this model is similar to a tree in the visualization of a node that follows a specific pathway to multiple outcomes. For example, the decision tree can begin with a node about BMI which branches off into a BMI over or under a specific category, which can then branch down into other health statistics. In this sense, the decision tree can be used to determine health status based on multiple data points and indicators. Decision trees are also seen within database design and specifically within hierarchical databases that are structured with central nodes that branch down to other nodes which they influence. These algorithms are also commonly employed as a method of visualizing predictive models by showing the potential outcomes and pathways that come from making an initial or central decision.
4. Naive Bayes
In addition to creating algorithms about specific outcomes, there are also algorithms that can be used for data forecasting and making predictions about the future. The Naive Bayes is one such algorithm and is based on the uses of Naive Bayes classifiers within a statistical analysis. These classifiers, which also serve as the basis for Bayesian network models, are simply used to assign labels to an instance based on some criteria. As a model based on probability, Naive Bayes makes predictions about what something is or will be, based on some criteria or data. the following outline includes the equation for Naive Bayes theory.
P ( A / B) = P ( B / A) * P (A) / P(B)
P ( A / B) = The likelihood of event A occurring, if B is true.
P ( B / A ) = The likelihood of event B occurring if A is true.
P ( A) and P(B) are the probabilities of events A and B as if they are independent of each other.
In this sense, the Naive Bayes algorithm offers a conditional probability model. Conditionals focus on statements that presume “If . . ., then . . .”, and are commonly utilized within computer programming and the creation of machine learning models. Within data science tools, such as Microsoft SQL Server, the Naive Bayes Prediction can be used to determine the likelihood of a specific event or outcome.
5. Random Forest
As the name suggests, random forest algorithms are a collection or gathering of trees, decision trees in particular. Due to the fact that this forest includes multiple trees, the decision that this model chooses is based on the class that the majority of the trees decide. These tree-shaped models are generally used for classification or regression, and this model helps to ensure the accuracy of predictions and decision-making. This algorithm is useful when making decisions about how to carry out a project or plan, such as whether or not to use a specific type of data science tool or even the best method of organizing a dataset. Some tools which include the option of using a random forest algorithmic model, include, but are not limited to, Scikit-learn, IBM SPSS Modeler, and Oracle.
6. Support Vector Machines (SVM)
Analyzing data for the purpose of classification, regression, and sorting, support vector machine algorithms view data points as support vectors which can then be used to find the optimal hyperplane for a dataset. Hyperplanes are the boundaries and borders of dimensional space and can be used to classify support vectors by creating a discrete or bounded area for selection. the purpose of support vector machines is to identify the hyperplane within a dimensional space of multiple sizes. Data science professionals can employ support vector machines by using the Scikit-learn library and this model is an excellent way of separating groups by class within a dataset.
7. K-Means
K-means algorithms are primarily used as a classification mechanism for dividing a dataset into specific groups based on criteria. By selecting a specific data point, this algorithm works to sort through a dataset and cluster those data points, or k-points, into k-clusters. These clusters then generate centroids, which hold the weight of these different data points and create a prototype of the cluster. Within data science, K-means algorithms can be used for signal processing, such as defining a set color palette within an image. In addition, K-means are effective at the creation of cluster analyses, which can be used to find groupings within a dataset. The K-means algorithms can also be employed with programming languages like Python as well as data science tools such as Tableau.
8. K-Nearest Neighbors (KNN)
Also used for both classification and regression analysis, the K-Nearest Neighbors (KNN) algorithm is primarily used to determine who to assign weight to variable “k” based on its relationship between neighboring values in the dataset. KNN algorithms search through a dataset to identify the k value and those that are similar to it, predicting the k based on all of these instances. Finding an instance of data also requires some knowledge of different measures for distance, such as Euclidean distance, Hamming distance, Cosine, etc.
The type of distance measure that you employ is also based on the scale and dimensionality of the dataset. It is important that you have a strong understanding of your data before choosing a measure. In addition to deciding on a data measure, you must also establish a value of k which is neither too large nor too small to determine the most accurate outcome and there are several techniques to simplify this process. Within data science, KNN algorithms can also be utilized with Python as well as in facial recognition software and technology.
9. Dimensionality Reduction
Within a database, there is a multitude of features and values stored within datasets. Features within a dataset correspond to specific pieces of data (or variables), and larger stores of data can have so many features that it becomes difficult to make clear associations or relationships between different sets of data. Known as a method of reducing the features of a dataset in order to increase its comprehensibility, dimensionality reduction algorithms are primarily used to reduce the size or representation of a dataset. Taking features from the higher dimensions of a dataset to a lower dimension, dimensionality reduction allows you to group data along a single line for ease of understanding and clarity. For data scientists, the methods of dimensionality reduction can be found when programming in R, as well as languages such as Python.
10. Artificial Neural Networks (ANN)
Commonly used within machine learning and artificial intelligence, artificial neural networks (ANN) help machines learn how to complete complicated tasks and decisions. Each of us has multiple neural pathways and clusters which make it easier for us to understand how to think and move and live. However, machines, like computers and robots, do not have those same innate abilities, therefore data science professionals have to build neural networks into these machines. Similar to other network models, artificial neural networks are made up of nodes and edges which create an assemblage that serves as the internal structure of the machine. These algorithms are used by data science professionals who do work with engineering and deep learning.
Want to learn how to use algorithms for data science and machine learning?
With the rise of recommendation systems and artificial intelligence, machine learning algorithms have become sought-after skillsets within the data science industry. It is important for data science students and professionals to not only have knowledge of the most commonly used algorithms but also training on how to use them. For those that want to know more about how algorithms are used within data science, Noble Desktop’s data science classes include several courses and bootcamps which reference machine learning and modeling. The Data Science Certificate teaches multiple machine learning algorithms and statistical methods for higher-level data analysis. For those that have an interest in financial technology, the FinTech Bootcamp also includes instruction on how to make predictions and projections about stocks, investments, and other forms of financial data.