Analyzing and classifying data is often tedious work for many data scientists when there are massive amounts of data. It even consumes most of their time and decreases their efficiency. Data scientists need to be smart, use cutting edge technologies, take calculated risks, and find out meaningful insights via supervised learning use cases that can discover opportunities to expand the business and maximize profits.
Data scientists & machine learning engineers rely upon supervised, unsupervised, and reinforcement learning. These methods give the best results in less time for classifying and analyzing data.
Supervised learning is the process of training an algorithm to map an input to a specific output. In this method, developers select the kind of information to feed within the algorithms to get the desired results. The algorithms get both inputs & outputs. Then the next step is creating rules that map the inputs with outputs. The training process continues until the highest level of performance is achievable.
If the mapping is correct, the algorithm is successful. If not, you can make necessary changes to the algorithms until it shows the right outputs. The prime objective is to scale the scope of data for new predictions about future outcomes.
Supervised learning is of two types – regression and classification.
Regression identifies the patterns in the sample data and predicts continuous outcomes. This algorithm understands the numbers, values, correlations, and groupings. This model is best for the prediction of products and stocks.
Regression models are of two types – Linear and Logistic regressions.
In linear regression, the algorithms assume that there lies a linear relationship between two variables, input (X) and output (Y). The input variable is an independent variable, whereas the output variable is a dependent variable. It uses the function, calculates, and plots the input to a continuous value for output.
In logistic regression, the algorithms predict the discrete values for the set of independent variables that it has on the list. The algorithm predicts the probability of the new data so that the output ranges between 0 and 1.
In the classification technique, the input data is labeled based on historical data. These algorithms are specially trained to identify particular types of objects. Processing and analyzing the labeled sample data, weather forecasting, identifying pictures is simple.
Some of the popular classification models are – Decision Trees, Naive Bayes Classifiers, and Random Forests.
In Decision Trees, the classifiers are references to feature values. It uses a tree-like model of decisions and their consequences. It’s an algorithm that only contains conditional control statements. Every branch in the decision tree symbolizes a feature of the dataset.
In Naive Bayes Classifiers, the algorithms assume that all the datasets are independent of each other. It works on large datasets and uses Direct Acyclic Graph (DAG) for classification purposes. Naive Bayes is suitable for solving multi-class prediction models. It’s quick and easy to save a lot of your time and handle complex data.
In Random Forests, the algorithm creates decision trees on data samples and then gets the prediction for each try until it selects the best solutions. It is an advanced version of decision trees because it reduces the overfitting cons of decision trees by averaging the result.
In Neural Networks, the algorithms get designed to cluster raw input and recognize patterns. Neural networks require advanced computational resources. It gets complicated when there are multiple observations. In other words, data scientists call it ‘black-box’ algorithms.
In the Support Vector Method (SVM), the algorithm separates hyperplanes as discriminative classifiers. SVM is closely related to kernel networks, and its output is in the form of an optimal hyperplane, best for two-group classification problems.
Supervised Learning Use Cases
Supervised learning has many applications across industries and one of the best algorithms for finding more accurate results. Here is a list of well-known applications of supervised learning.
Spam detection – supervised learning methods have immense use of detecting mail, whether it is spam or not. Using different keywords and content, it recognizes and sends a specific email to the relevant categorical tabs or into the spam category.
Bioinformatics – one of the best applications of bioinformatics is the storage of biological information of human beings. That includes – fingertips, iris textures, eyes, swabs, and so on. All the smart devices are capable of storing fingerprints so that every time you want to unlock your devices, it asks to authenticate either through fingertips or facial recognition.
Object Recognitions – one of the popular applications is Recatch (prove you are not a robot.) It is where you have to choose multiple images as per the instruction to get confirmed that you are a human. You can only access if you can identify correctly, or else you have to keep on trying to get the correct identifications.
Disadvantages of Supervised Learning.
- Computation time is vast for supervised learning.
- Unwanted data downs efficiency.
- Pre-processing of data is no less than a big challenge.
- Always in need of updates.
- Anyone can overfit supervised algorithms easily.
Supervised learning use cases use labeled data to train a machine or an application, regression, and classifications techniques to develop predictive data models that have multiple applications across all domains and industries. And even if in our daily life, we all use them. Supervised learning requires experienced data scientists to build, scale, and update the models.
If the algorithms go wrong, results will be inaccurate. Therefore, the selection of relevant data is crucial for supervised learning to work efficiently. Selecting the right and relevant insights are always vital for a training set, and the real-life applications of supervised learning are tremendous.
Reference Reading – https://www.excelr.com/blog/data-science/regression/simple-linear-regression
Article by Palak Airon of ExcelR
Learn more about machine learning engineering platforms and skills at ODSC East 2022
We just listed off quite a few machine learning engineering platforms, skills, and frameworks. It’s not expected to know every single thing mentioned above, but knowing a good chunk of them – and how to apply them in business settings – will help you get a job or become better at your current one.
At ODSC East 2022, we have an entire track devoted to machine learning engineering and deep learning. Learn ML engineering skills and platforms like the ones listed above. Here are a few sessions scheduled so far:
- Dealing with Bias in Machine Learning: Thomas Kopinski, PhD | Professor for Data Science | University of South Westphalia
- Mastering Gradient Boosting with CatBoost: Nikita Dmitriev | Member of CatBoost Team | Yandex
- Network Analysis Made Simple: Eric Ma, PhD | Author of nxviz Package
- Building and Operating Cloud Native Analytics Systems at Scale: Scott Haines | Software Architect | Twilio
- End to End Machine Learning with XGBoost: Matt Harrison | Python & Data Science Corporate Trainer & Consultant | MetaSnake
- Automation for Data Professionals: Devavrat Shah, PhD | Professor, Founding Director, Co-founder, and CTO | Statistics and Data Science, MIT & IkigaiLabs
- Self-supervised Representation Learning for Speech Processing: Abdel-rahman Mohamed, PhD | Research Scientist | Facebook AI Research
- Machine Learning for Causal Inference: Stefan Wager, PhD | Assistant Professor | Stanford Graduate School of Business
- Deep Dive Workshop for Apache Superset: Srinivasa Kadamati | Committer, Senior Data Scientist / Developer Advocate, Apache Superset | Apache Superset, Preset
- From Experimentation to Products: The Production ML Journey: Robert Crowe | TensorFlow Developer Engineer | Google
- The Future of Software Development Using Machine Programming: Justin Gottschlich, | Ph.D. | Founder, CEO & Chief Scientist | Merly.ai