Remove Events Remove K-nearest Neighbors Remove Support Vector Machines
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Top 8 Machine Learning Algorithms

Data Science Dojo

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. K-Nearest Neighbors (KNN): This method classifies a data point based on the majority class of its K nearest neighbors in the training data.

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8 of the Top Python Libraries You Should be Using in 2024

ODSC - Open Data Science

Interested in attending an ODSC event? Learn more about our upcoming events here. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. Subscribe to our weekly newsletter here and receive the latest news every Thursday.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Journey to AI blog

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. But what is an anomaly and why is detecting it important?

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Everything to know about Anomaly Detection in Machine Learning

Pickl AI

Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour. Finding anomalous occurrences that might point to intriguing or potentially significant events is the aim of anomaly detection.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

JSON (JavaScript Object Notation): A lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. Joint Probability: The probability of two events co-occurring, often used in Bayesian statistics and probability theory.

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From prediction to prevention: Machines’ struggle to save our hearts

Dataconomy

Hybrid machine learning techniques integrate clinical, genetic, lifestyle, and omics data to provide a comprehensive view of patient health ( Image credit ) The choice of an appropriate model is critical in predictive modeling. Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models.

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Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

Anomaly detection ( Figure 2 ) is a critical technique in data analysis used to identify data points, events, or observations that deviate significantly from the norm. For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data.