Supervised Machine Learning What is Supervised Machine Learning?
Supervised machine learning is a type of artificial intelligence in which the model is trained with labeled data. Here, the algorithm is fed with input-output pairs, allowing it to learn the mapping between inputs and then its corresponding outputs.
- How does supervised machine learning work?
- The process of supervised learning
- Types of supervised machine learning
- Partner with HPE
How does supervised machine learning work?
At the time of training with supervised machine learning, the algorithm tweaks its parameters to lessen the difference between expected and actual outputs that occur. Once trained, the model can generate predictions on previously unknown data by generalizing patterns from the training data. Common supervised learning problems include classification, which predicts a categorical label, and regression, which predicts a continuous value.
The process of supervised learning
In supervised learning, an essential paradigm of machine learning, algorithms learn from labeled data to make predictions or decisions. This process has important key stages, starting with data collection and preprocessing, followed by model training, testing, and deployment. Each step plays a crucial role in developing and deploying effective models for various real-world applications.
- Data Collection and Processing: A lot of raw data needs to be trimmed and modified so that the ML model can efficiently process the data. Typically, a data analyst or data scientist will look at data and see what is missing as well as see if the data needs more context or add more data to the set to complement the ML model.
- Training Process: The dataset is fed into the machine learning algorithm, which then learns to identify patterns, correlations with other regions of data, and relationships between the input features and output labels. The algorithm adjusts its internal parameters to find the best mathematical function that maps the input features to the output labels. A data scientist would review the first iteration and adjust the process until it is ready for model testing.
- Model Testing: After the machine learning algorithm is calibrated, it begins testing on new or unseen data to see if it comes up with a similar or consistent answer as in the training phase. If the output is less than desirable the data scientist will adjust the algorithm until a consistent answer is reached or need to start the process over.
Model Execution: After receiving consistent results, the model would then be deployed on new data that comes to the business and is used to predict the new outcome, forecast budget or revenue, or observe the next trend.
Types of supervised machine learning
Classification: Classification is a type of supervised learning where the algorithm categorizes data into predefined classes or categories based on input features. It learns from labeled training data and then predicts the class labels for new, unseen data points.
Industries:
- Healthcare: Classifying medical images for disease diagnosis.
- Finance: Categorizing transactions for fraud detection.
- E-commerce: Grouping customers for targeted marketing.
Regression: Regression is a supervised learning technique used to predict continuous numerical values based on the relationship between input features and output variables. It learns from labeled training data to estimate this relationship and make predictions on new data.
Industries:
- Real Estate: Predicting house prices based on property features.
- Healthcare: Forecasting patient recovery times based on treatment.
- Energy: Estimating power consumption for resource planning.
Partner with HPE
HPE partnerships can help scale AI and machine learning models. Here are some of the products that can accelerate AI adoption in your business:
- HPE machine learning development environment: It has everything needed to design, train, and deploy machine learning models. It contains machine learning-optimized IDEs, libraries, and frameworks.
- HPE machine learning data management software: This tool organizes machine learning data. It streamlines data import, preprocessing, labeling, and versioning to curate and make data easily accessible for model training and testing.
- AI, ML, and Data Analytics Products: HPE solutions are built for AI, machine learning, and data analytics. This can include hardware accelerators, specialized servers, storage solutions, and AI-specific software platforms.
HPE partners can help your organization use AI and machine learning for better decision-making, automation, and innovation. HPE can assist you expedite AI application development and deployment, improving industrial efficiency and competitiveness.
Supervised vs Unsupervised
| Aspect | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Definition | Supervised learning is a type of machine learning where the algorithm learns from labeled data, which includes both input data and corresponding output labels. The goal is to predict or classify new data based on the patterns learned from the labeled examples. | Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. It explores the structure and patterns within the data without explicit guidance or feedback and aims to uncover hidden insights or groupings. |
| Training Data | Requires labeled data (input-output pairs). | Does not require labeled data. |
| Goal | Predicts or classifies based on labeled data. | Finds hidden patterns or structures in data. |
| Feedback | Receives feedback during training. | No feedback during training. |
| Output | Output is known and predefined. | Output is not predefined or known. |
| Example Applications | Spam detection, image recognition, sentiment analysis. | Clustering, anomaly detection, dimensionality reduction. |
