Read: 168
Original Article:
algorithms are at the heart of modern technology, making predictions and decisions based on data inputs. They can be broadly categorized into two types: supervised learning and unsupervised learning.
In supervised learning, the algorithm learns from labeled input data that includes both input variables and output or target variables. The model tries to find a function that best maps the input to the output by looking for patterns in the trning set provided.
Unsupervised learning involves analyzing raw data pre-existing labels or categories. It is about discovering hidden structures within the data through finding patterns, associations, correlations, etc.
Some commonly used algorithms include linear regression for predicting continuous outputs, logistic regression for binary classification tasks, decision trees and random forests for both classification and regression problems, support vector s for classification and regression tasks, K-nearest neighbors algorithm for both, neural networks including deep learning for complex problem solving, and clustering algorithms like K-means or hierarchical clustering to find natural groupings within the data.
While these concepts are basic building blocks in , they can be adapted into a wide range of practical applications from healthcare diagnostics to recommation systems in e-commerce. A solid understanding of these fundamentals is crucial before advancing to more sophisticatedand techniques.
algorithms serve as the foundation of contemporary technology, crafting predictions and decisions based on data inputs. These algorithms can be broadly categorized into two mn types-supervised learning and unsupervised learning-each with distinct approaches and applications.
Supervised learning involves trning algorithms using labeled input data that includes both features input variables and labels or target values. The model identify the underlying function by recognizing patterns within the provided trning set, mapping inputs to desired outputs.
Unsupervised learning takes a different approach by analyzing raw data without predefined labels or categories. Its primary goal is to uncover hidden structures in the data through identifying patterns, associations, correlations, and other features.
A variety of commonly employed algorithms exist for various purposes:
Linear regression predicts continuous outcomes.
Logistic regression tackles binary classification tasks efficiently.
Decision trees and random forests are versatile tools suitable for both classification and regression problems.
Support vector s excel at handling both classification and regression challenges effectively.
The K-nearest neighbors algorithm performs well in classification and regression contexts alike.
Neural networks, including deep learning techniques, address complex problem-solving scenarios.
Clustering algorithms like K-means or hierarchical clustering are used for identifying natural groupings within the dataset.
Although these foundational concepts form the basics of , they pave the way for numerous practical applications ranging from healthcare diagnostics to sophisticated recommation systems in e-commerce. A thorough comprehension of these fundamental ideas is essential before delving into more advancedand techniques.
This article is reproduced from: https://link.springer.com/content/pdf/10.1007/978-3-319-97310-4.pdf
Please indicate when reprinting from: https://www.o009.com/Chess_and_Card_Game_Three_Kingdoms_Kill/algo_basics_undertstanding_and_decoding.html
Machine Learning Algorithms Overview Supervised vs Unsupervised Learning Linear Regression Explained Logistic Regression for Classification Decision Trees and Random Forests Application Neural Networks in Practical Scenarios