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dimensionality-reduction-technique

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Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most …

  • Updated Dec 31, 2019

This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques

  • Updated May 10, 2020
  • Python
different_processings_for_ML

This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.

  • Updated Jan 26, 2024
  • Jupyter Notebook

This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis (PCA).

  • Updated Jan 8, 2024
  • Jupyter Notebook

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