https://github.com/mohammad95labbaf/umap_breast_cancer

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.
https://github.com/mohammad95labbaf/umap_breast_cancer

breast-cancer-wisconsin decision-tree decision-tree-classifier dimensionality-reduction dimensionality-reduction-technique eda expolatory-data-analysis kernel-pca lle locally-linear-embedding principal-component-analysis umap

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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.

• Host: GitHub
• URL: https://github.com/mohammad95labbaf/umap_breast_cancer
• Owner: mohammad95labbaf
• Created: 2024-01-12T15:28:52.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-01-26T06:02:53.000Z (11 months ago)
• Last Synced: 2024-01-26T07:24:10.075Z (11 months ago)
• Topics: breast-cancer-wisconsin, decision-tree, decision-tree-classifier, dimensionality-reduction, dimensionality-reduction-technique, eda, expolatory-data-analysis, kernel-pca, lle, locally-linear-embedding, principal-component-analysis, umap
• Language: Jupyter Notebook
• Homepage:
• Size: 2.69 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# UMAP_breast_cancer

Dimensionality reduction and classification are two key steps in machine learning workflows, often employed to address challenges associated with high-dimensional data. Let's explore these concepts individually and their integration in the context of a classification task.

### Dimensionality Reduction:

**Definition:**

- **Objective:** Reduce the number of features (dimensions) in a dataset while preserving its essential information.

- **Purpose:**

  - **Curse of Dimensionality:** High-dimensional data can lead to increased computational complexity, sparsity, and overfitting.

  - **Visualization:** Facilitate visual exploration of data in lower-dimensional spaces.

- **Methods:**

  - **Unsupervised Techniques:** PCA, KPCA, LLE, UMAP.

  - **Supervised Techniques:** Supervised UMAP.

### Classification:

**Definition:**

- **Objective:** Assign predefined labels or classes to input data points based on patterns learned from training data.

- **Purpose:**

  - **Prediction:** Predict the class of new, unseen instances.

  - **Decision Making:** Inform decision-making based on learned patterns.

- **Algorithms:**

  - **Decision Trees

### Integration of Dimensionality Reduction and Classification:

**Motivation:**

- **Curse of Dimensionality:** High-dimensional data can lead to increased computational complexity, sparsity, and overfitting.

- **Improved Generalization:** Reduce noise and irrelevant features, focusing on the most informative ones.

**Steps:**

1. **Data Preprocessing:**

   - **Standardization/Normalization:** Ensure features are on a similar scale.

   - **Splitting:** Divide the dataset into training and testing sets.

2. **Dimensionality Reduction:**

   - Choose an appropriate method based on the nature of the data and the task.

   - **Unsupervised:** PCA, KPCA, LLE, UMAP.

   - **Supervised:** Supervised UMAP.

3. **Classification:**

   - Choose a classification algorithm based on the problem and data characteristics.

   - **Train the Model:** Use the reduced-dimensional data from the training set to train the classifier.

   - **Evaluate Performance:** Assess the model's performance on the test set.

**Benefits:**

- **Improved Efficiency:** Reduced dimensionality often leads to faster training and prediction times.

- **Enhanced Interpretability:** Easier to interpret and visualize data in lower-dimensional spaces.

- **Better Generalization:** Reduced risk of overfitting and improved generalization to new data.

**Considerations:**

- **Interpretability:** Balance the need for interpretability with model complexity.

In summary, dimensionality reduction and classification are intertwined in machine learning pipelines, working together to address challenges associated with high-dimensional data and improve the efficiency and effectiveness of classification models. The choice of methods depends on the specific characteristics of the data and the goals of the classification task.