What Is Dimensionality Reduction?

Dimensionality Reduction Assignment help

It is the process of reducing the number of independent variable, if your problem related to dimensionality reduction then we will help you to do your task and fit dimensionality reduction algorithms easily in your code. Below the some features which makes it better to improve machine learning accuracy and for which you use this in machine learning algorithms.

  • Reducing Dimensionality of independent variables helps in many ways.

  • Remove multi-collinearity to improve ML model performance

  • Helps Reduce Over fitting

  • Decreases Computational time for fitting models

  • Makes Visualization easier

  • Decreases Storage requirements

  • Avoid Curse of dimensionality

Here we can say that dimensionality reduction plays a significant role in analyzing data.

It use different Dimensionality Reduction techniques:

  • Feature Elimination

  • Feature Extraction(PCA, t-SNE)


PCA

Creates New variables using linear combinations of old variables

Is designed to create variables that are independent of one another

also manages to tell us how important each of these new variables are

this "important", helps us to choose how many variables we will use

















  • Scale the data and compute the covariance matrix

  • Break the covariance matrix into magnitude and direction. Eigen Vectors and the Eigen Values of the covariance matrix can be thought of as the natural axis/directions and magnitudes along those axis, of the data

  • The eigen value also can we used to calculate the percentage of variation explained by each component

  • Sort the Eigen values into descending order and calculate the cumulative percentage of variation explained

  • Pick the number of principal components you will use

  • Transform to new variable







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