Evaluation Metrics | L-01 | Basic Overview
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Welcome to our playlist on "Evaluation Matrices in Machine Learning"! In this series, we dive deep into the key metrics used to assess the performance and effectiveness of machine learning models. Whether you're a beginner or an experienced data scientist, understanding these evaluation metrics is crucial for building robust and reliable ML systems.
📷 Check out our comprehensive guide to Evaluation Matrices in Machine Learning, covering topics such as:
Accuracy
Precision and Recall
F1 Score
Confusion Matrix
ROC Curve and AUC
MSE (Mean Squared Error)
RMSE (Root Mean Squared Error)
MAE (Mean Absolute Error)
Stay tuned as we explore each metric in detail, discussing their importance, calculation methods, and real-world applications. Whether you're working on classification, regression, or another ML task, these evaluation matrices are fundamental to measuring model performance accurately.
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I just watched your videos - they're fantastic! For someone completely new to this, adding a coding session that walks through implementation would be a great addition.
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The neighborhood theory is devoted to description and solving follows problems:
- Embedding of graph systems or systems with quasi-distance in family of Euclidian spaces;
- Partition the system into intersecting subsystems upon the principle of proximity of thepoints;
- Optimal structurization of the system through the neighborhood criterion;- Strength of connection and mutual influence between the neighboring points;
- Internal and boundary points;
- Quasi-metric of neighborhood as minimal length of the broken line (geodesic) goingthrough the neighboring points;
- Curvature, difference (differential) operators, Voronoi regions, the neighboring sphericallayers, density of the geodesics;
- The Bayesian probabilistic model interpreting the a priori measure as a geometric spaceand the a posteriori one as a set of events in time;
- Dimension, volume and measure for the a priori geometric space;
- Entropy for the Bayesian probabilistic model as functional of the system;
- The problems of regression and classification;
- The local macroscopic region that define the neighborhood structure for the select pointwith acceptable accuracy;
- Distribution of density, number of the neighboring points and dimension;- Diffusion equation;
- Clustering problem on the basis of connectivity coefficient (internal clustering);
- Clustering problem on the basis of the extent to which the points are internal or boundary(external clustering);
- Parameterization of distances in the systems;
- The models of multisets and strings;
- Generative model;- Probability and time;
- The complex Markov chains and influence graph;
- Geometries on the systems with quasi-metric. (PDF) Neighborhood Theory. Available from: https://www.researchgate.net/publication/377731066_Neighborhood_Theory
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