Nowadays, data has become the mainstay of business planning and decision-making. Businesses need to have mechanisms and processes in place that enable them to extract, gather, store, clean, analyze, and summarize data. Also, they need to be aware of the latest innovations in the field of data management. Data science and machine learning are two such innovations that have transformed the way data is being used by businesses for improving operations and processes.
Let’s gain more knowledge about these data-related innovations.
Data Science
Data science is an interdisciplinary field that makes use of algorithms, procedures, and processes to examine and evaluate huge amounts of data to uncover hidden patterns, generate insights, and guide decision-making. To build predictive models, data scientists use complex and advanced machine learning algorithms for sorting through, organizing, and learning from structured and unstructured data alike.
Importance of Data Science
There is a huge amount of data available in a variety of data formats. This added complexity makes it difficult for businesses to gain useful insights without the use of machine learning techniques. Businesses felt the need for such techniques, methods, or tools that can help them analyze data more efficiently and quickly. This is a field that is a combination of complex Machine Learning techniques integrated with various tools such as Microsoft Power BI, SAP BusinessObjects, Sisense, and TIBCO Spotfire. Such tools help businesses in decision-making, finding new patterns in data, and discovering new ways of Predictive Analysis.
Machine Learning
Machine learning is a type of artificial intelligence (AI) that allows software applications and computer systems to automatically learn, adapt, and improve from experience and become more accurate at predicting outcomes without being explicitly programmed to do so. The applications and systems make use of algorithms and statistical models for performing data analysis and drawing inferences from patterns in data.
Importance of Machine Learning
Businesses need ML to make high-value predictions that can guide better decisions as well as smart actions in real time without requiring human intervention. The ML technology enables businesses to analyze large chunks of data accurately and efficiently in an automated manner. ML has changed the way data extraction and data interpretation work by making use of automatic sets of generic methods instead of traditional statistical techniques.
ML and data science can work hand in hand, but there are some differences between the two.
⦁ Data Science combines tools, algorithms, and ML techniques. They help businesses in finding common hidden patterns from the raw data. Compared to Data Science, ML is a branch of computer science that deals with system programming for automatic learning and bettering with experience.
⦁ Data Science extracts useful business insights from huge amounts of data through various scientific methods, algorithms, and processes. Compared to Data Science, ML is a system with the ability to learn from data through self-improvement and without a programmer explicitly doing coding for the same.
⦁ Data Science can work even with manual methods, though they are not very useful, but ML algorithms are hard to implement in a manual framework.
⦁ Data science is not a subset of AI, but ML technology is a subset of AI. Though data science is not a subset of AI, it is related to AI because while data science involves attempting to solve complex problems with data, AI involves the development of algorithms to find solutions to these problems. ML is a subset of AI as it is one of the AI algorithms that has been developed to mimic human intelligence. ML is an application of AI.
⦁ Data science techniques help businesses create insights from data that deal with all the complexities of the real world. Compared to data science techniques, the ML method helps businesses in predicting the outcome of new database values.
Types of Machine Learning Algorithms: –
At its most basic, ML utilizes programmed algorithms that receive and analyze input data for predicting output values within an acceptable range. As new data is provided to these algorithms, they learn and optimize their operations to achieve enhanced performance, thus developing intelligence over time. There are four types of ML algorithms, namely supervised, semi-supervised, unsupervised, and reinforcement.
⦁ Supervised learning
In a supervised learning algorithm, the machine is taught with the use of an example. The operator gives the ML algorithm a known dataset with required inputs and outputs. The algorithm must find a method to determine how to arrive at the inputs and outputs. The operator is aware of the correct answers to the problem, but the algorithm recognizes patterns in the data, learns from the observations made by it as well, and makes predictions. The algorithm makes predictions, which are then corrected by the operator. This process continues till the algorithm can achieve a high level of performance/accuracy.
Supervised learning is further divided into these three categories: –
⦁ Classification: In classification-related tasks, the ML program must conclude values observed by it. Also, it will determine to what category new observations belong.
⦁ Regression: In regression tasks, the ML program must estimate and understand relationships between various variables. Regression analysis emphasizes one dependent variable and a series of other changing variables, making it particularly useful for prediction and forecasting.
⦁ Forecasting: Forecasting is the process of making predictions about a business’s future based on its past data as well as its present data. It is usually used to analyze trends.
Here are some applications of supervised learning.
Finance
Supervised ML is used by banks and other financial institutions for making predictions. These include predicting stock market volatility based on past trends performed during volatile periods. Supervised ML is also used for making predictions for specific stocks over a longer period. Financial institutions also utilize supervised ML for detecting fraud and checking anti-money laundering compliance. They also use it for forecasting stock prices and making reliable investment decisions.
Bioinformatics
Bioinformatics is one of the most widely used supervised learning applications. Bioinformatics involves the study of how individuals retain biological knowledge such as eye texture, fingerprints, earlobes, etc. Mobile phones are now clever enough to comprehend users’ biological data and then verify them to increase system security.
Speech recognition
It is the type of program where a user may convey his voice to the program, and it will identify him. Digital assistants such as Siri or Google Assistant are the most well-known real-world gadgets, which respond to the term only with a user’s voice.
Spam detection
This tool is utilized to prevent the sending of fictitious or machine-based communications. Gmail has an algorithm that learns numerous wrong terms. The OnePlus Messages App asks a user to specify which terms should be avoided and the keyword accordingly will prevent these texts from the app.
⦁ Semi-supervised learning
Semi-supervised learning is like supervised learning, but it instead uses labeled as well as unlabeled data. Labeled data is information with meaningful tags, so the algorithm can understand the data. In comparison to the labeled data, unlabeled data lacks that information. Unlabeled data has no targets or labels to predict, there are only features to represent them. By using this combination, ML algorithms can uncover patterns in unlabeled data.
These are some applications of semi-supervised learning.
⦁ Banking: In the Banking Sector, security is of utmost importance. Semi-supervised learning can help banks in performing various activities, including identifying cases of extortion. The developer can use some examples of extortion cases as a labeled data set. The rest of the data related to customers needs to be labeled with Semi-Supervised Learning. In this scenario, the framework is prepared based on current samples and algorithms provided by the developer. Here semi-supervised algorithms work best with controlled frameworks as well as uncontrolled frameworks.
⦁ Image and Speech Analysis: Images and audio files are generally not labeled. Labeling them is an arduous task and an expensive affair. With the help of human expertise, businesses can label a small data set. After the data is trained, businesses can then implement semi-supervised learning to label the rest of the audio and image files. Thus, they can improve image and speech analytic models.
⦁ Web Content Classification: The internet contains billions of websites with different classified content. Providing this information to web users requires a vast team of human resources who can organize and classify the content on the web pages. Semi-supervised learning can help by labelling the content and classifying it which thus helps to improve user experience. Google and other search engines make use of a semi-supervised learning model for labeling and ranking web pages in their search results.
⦁ Unsupervised learning
In an unsupervised learning framework, the ML algorithm studies data for identifying patterns. There is no human operator or answer key to give instructions to the machine. Instead, the machine determines the relationships and correlations by analyzing available data. In an unsupervised learning process, the ML algorithm is allowed to interpret huge data sets and address that data. The algorithm attempts to organize this data in some way to describe its structure. This might mean grouping the data into clusters or arranging it in a manner that looks more organized.
As the algorithm assesses an increased amount of data, its ability to make decisions based on that data gradually improves and becomes more refined.
Unsupervised learning is divided into these two categories: –
Clustering: Clustering involves grouping sets of similar data based on defined criteria. It is useful for segmenting data into several groups and doing an analysis of each data set to find patterns. Clustering is an unsupervised task that aims to describe the hidden structure of the objects. Each object is described by a set of characters that are called features. The first step of dividing objects into clusters is used for defining the distance between the different objects. Defining an adequate distance measure is the key to the success of the clustering process.
Clustering is usually used when you do not have a particular outcome variable that you are trying to predict. Instead, it is utilized when you have a set of features that you want to use to find collections of observations with similar characteristics.
Dimension reduction: Dimension reduction decreases the number of variables being considered to find the exact information needed. In the field of ML, the dimension reduction process is applied to highly dimensional data. The purpose of this process is to decrease the number of features being considered, where each feature is a dimension that partly represents the objects. Dimension reduction is important for this reason. Due to the addition of more features, the data becomes very sparse, and analysis suffers from the curse of dimensionality. Moreover, it is easier to process smaller data sets.
Dimension reduction is a data preparation technique performed on data before modeling. It might be performed after cleaning and scaling of data and before training a predictive model.
Here are some real-world applications of unsupervised learning.
Visualization
The process of making diagrams, charts, graphs, photos, etc. to present information is known as visualization. Unsupervised ML can be used for the implementation of the visualization strategy.
Anomaly detection
Anomaly detection is the discovery of unusual occurrences, observations, or things that raise suspicion by deviating greatly from regular data.
⦁ Reinforcement learning
Reinforcement learning emphasizes regimented learning processes. Here the ML algorithm is given a set of actions, parameters, and end values. After the rules are defined, the ML algorithm attempts to explore various options and possibilities. It also monitors and evaluates every result to determine which one is optimal.
The reinforcement learning algorithm teaches the machine trial and error. It learns from past experiences and begins adapting its approach in response to the situation to attain the best possible result.
Here are some real-time applications of reinforcement learning.
Finance
Several reinforcement learning techniques can help financial organizations generate more return on investment, improve customer experience, lower costs, etc. Portfolio management is one of the most popular applications of reinforcement learning in the financial sector. Reinforcement learning helps investors in building a portfolio management application that enables them to evaluate the financial market in a detailed manner. It also enables them to make significantly more accurate predictions regarding stocks and other such investments, thus leading to better results. Robo-advisors are a good example of portfolio management applications that allow investors to generate more accurate results with time.
Marketing
In the field of digital marketing, reinforcement learning facilitates the development of personalized recommendation systems. These enable marketers of companies to provide a personal touch to customers with respect to their purchase decisions. With the help of systems, marketers can deliver high-quality recommendations to customers that resonate with their specific behaviors, needs, and preferences.
Robotics
Reinforcement learning helps to endow robots and machines with several abilities, including the ability to learn, adapt to, and improve in tasks where constraints change dynamically based on autonomous learning and exploration.
The Risks of Machine Learning Models
Like traditional statistical models such as logistic regression, ML models can also expose a firm to risk, eventually leading to adverse consequences and poor decisions. This happens if a model has an error in its design or construction, if it performs poorly, or is used inappropriately. The risks of ML models are qualitatively like traditional models’ risks, but they depend on high-dimensional data, dynamic retraining, the opacity of the transformation logic, and feature engineering because of which unexpected results can arise. Also, this can make risks more difficult to recognize and assess.
As with traditional models, poor performance can arise in ML models too due to implementation errors. These include errors related to calibration and poor data quality. In ML, the model complexity makes it more difficult to assess whether the results arising from the model can be generalized beyond the data utilized for training. The results may not be usually applicable in the case of the model underfitting or overfitting the data in relation to a set of performance criteria.
Underfitting means that the model does not capture the data well in a sample relative to the performance criteria. Overfitting means that the model fits the training data increasingly well relative to a set of performance criteria. Moreover, it displays poor prediction performance when it is tested out of the sample. Poor data availability or quality can undermine model fit and lead to a lack of fairness and sampling bias.
Also, like traditional models, ML models can be used inappropriately, thus giving rise to unintended consequences. The model result should be informative and relevant in understanding whether the desired business outcome is attained. Risk can occur because the goal as defined by the ML algorithm is not aligned to the real-world business problem statement.
The model’s intended use may also not align with real-world applications because of issues noted later regarding the availability, quality, and representativeness of data. As a result, the output’s informativeness of the business decision is overstated. Alternatively, the business goal quantified by the algorithm may be aligned with the business problem, but it may not account for all relevant considerations, which in turn can bring undesired consequences such as a lack of fairness.
If these risks are not addressed and mitigated on time, they can lead to poor planning, forecasting, and decision making which can consequently lead to financial losses, loss of customer trust, and other unfavorable outcomes for businesses.
Therefore, model validators and model developers need to monitor and analyze the entire ML model life cycle. These include various steps of the process like creation, configuration, experimentation, tracking the different experiments, and deployment of a model. If any deviations, inconsistencies, or errors are observed during testing, performance, implementation, and other aspects, the models need to be rectified, modified, and adapted accordingly.
By following a comprehensive and enhanced model risk management approach, businesses can achieve effective management, resolution, mitigation, and negation of ML model risks. Such an approach includes making policy decisions on what to include in a model inventory and determining risk appetite, risk tiering, roles and responsibilities, and model life-cycle controls as well as adopting the associated best model-validation practices. By adopting new model validation frameworks or enhancing the existing model validation frameworks that are overseen by experienced and knowledgeable financial advisors, businesses can achieve improved and effective machine-learning model risk management.
Now that businesses are more enlightened about the usefulness of data science and ML for forecasting, planning, and decision-making, they can begin their ML journey and navigate it to success with useful insights from financial specialists and advisors of Leka Research Institute. Call them today at +1(855)-558-4774 to learn more.