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
