Dimensionality Reduction

What is dimensionality reduction?

Dimension Reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions ensuring that it conveys similar information concisely. These dimensionality reduction techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.

What are the different dimensionality reduction techniques?

There are multiple dimensionality reduction techniques for machine learning. There are name of some of them:

1. Missing Value Ratio
2. Low Variance Filter
3. High Correlation Filter
4. Random Forest
5. Backward Feature Elimination
6. Forward Feature Selection
7. Factor Analysis
8. Principal Component Analysis (PCA)
9. Independent Component Analysis
10. Methods Based on Projections
11. t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. UMAP

Know about Data Science

 

 

Data science is a fuzzy concept. There are and have been many definitions or attempts at definitions around, and it doesn’t need to surprise that some of these have been visually represented.  In the center of the picture is data science and it is the result of the combination of hacking skills, mathematics and statistics knowledge and substantive expertise.

Data science is now defined through its relation to other disciplines, such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Big Data (BD) and Data Mining (DM).

Data science applications-

• Identifying and predicting disease
• Personalized healthcare recommendations
• Optimizing shipping routes in real-time
• Getting the most value out of soccer rosters
• Finding the next slew of world-class athletes
• Stamping out tax fraud
• Automating digital ad placement
• Algorithms that help you find love
• Predicting incarceration rates