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Problem-solving tools offered by digital technology

Data Science Dojo

Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2 1) Big O Notation 2) Search 3) Sort 3)–i)–Quicksort 3)–ii–Mergesort 4) Stack 5) Queue 6) Array 7) Hash Table 8) Graph 9) Tree (e.g.,

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Feature scaling: A way to elevate data potential

Data Science Dojo

However, it can be very effective when you are working with multivariate analysis and similar methods, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), K-means, Gradient Descent, Artificial Neural Networks (ANN), and K-nearest neighbors (KNN).

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

Some common models used are as follows: Logistic Regression – it classifies by predicting the probability of a data point belonging to a class instead of a continuous value Decision Trees – uses a tree structure to make predictions by following a series of branching decisions Support Vector Machines (SVMs) – create a clear decision (..)

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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk

Flipboard

Feature selection via the Boruta and LASSO algorithms preceded the construction of predictive models using Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, LightGBM, and XGBoost. Demographic data, physiological status, and non-invasive test indicators were collected.

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Anomaly detection using machine learning and adopted digital twin concepts in radio environments

Flipboard

XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81).

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3 Greatest Algorithms for Machine Learning and Spatial Analysis.

Towards AI

For geographical analysis, Random Forest, Support Vector Machines (SVM), and k-nearest Neighbors (k-NN) are three excellent methods. Scalability: Verify that the algorithm can manage increasing data quantities and, if required, be applied to distributed systems. So, Who Do I Have?

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Classifiers in Machine Learning

Pickl AI

Examples include: Spam vs. Not Spam Disease Positive vs. Negative Fraudulent Transaction vs. Legitimate Transaction Popular algorithms for binary classification include Logistic Regression, Support Vector Machines (SVM), and Decision Trees. Each instance is assigned to one of several predefined categories.