Supervised learning
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Supervised learning is a type of machine learning focused on deriving predictions from labeled input-output pairs. It involves a process that includes historical data, training and test datasets, and validation to optimize model accuracy. The document also briefly mentions other machine learning types such as unsupervised learning and reinforcement learning.
Supervised learning
- 2. Machine learning is a branch of computer science that focuses on the research and development of algorithms that can learn from and predict data. Rather of following purely static programme instructions, such algorithms construct a model from sample inputs in order to generate data-driven predictions or choices. Machine Learning
- 3. Types of Machine Learning Unsupervised Reinforcement Supervised
- 4. Supervised Learning is learning a function that maps an input to an output based on data input-output pairs. It infers a function from labeled training data consisting of a set of training data Types of Machine Learning Unsupervised learning more specifically, clustering include: Customer segmentation, or understanding different customer groups. Unsupervised learning is commonly used for finding meaningful patterns and groupings inherent in a given or collected date set. Reinforcement is a type of Machine Learning algorithm which allows software agents and machines to automatically determine the ideal behavior within a specific context, to maximize its performance.
- 5. Supervised Learning Process Flow . Historical data . Random Sampling . Training Dataset . Test Dataset . Machine Learning . Prediction, Validation . Statistical Model . Model validation outcome Training and Validation
- 6. Supervised Learning Process Flow . New Data . Statistical model . Prediction outcome Prediction Prediction
- 7. Learning Training Testing Training data Learning Algorithm Supervised Process 2 Steps Model Test data Accuracy
- 8. Topics for next Post Support Vector Machines Linear regression Logistic regression Stay Tuned with