Analyzing effect of dimensionality reduction on accuracy of different classifiers on different types of datasets

[parimal173]

It will be a document showing the Effect of Dimensionality Reduction on the accuracy of different classifiers.
The document will have simulations On High dimensional dataset of different shapes:
Each dataset is synthesized from sklearn established Datasets. Each dataset has 1000 dimensions with only 2 dimensions of data and rest are noise dimensions.

Questions to answer:

1. Analyzing how dimensionality reduction helps in classification for different classifiers.
2. Analyzing how classifiers perform with a different number of reduced datasets from the main high dimensional dataset.

Pipeline to be followed:

1. Defining a dataset with sklearn established synthetic datasets with High dimensions.
2. Performing classification on data and measuring accuracy for quantification of the process.
3. performing the Dimensionality reduction technique keeping varying numbers of reduced dimensions.
4. Checking the performance of classification again after reducing dimension after each iteration.

The output of the PR would be a figure showing different datasets, comparing accuracies of different classifiers with and without dimensionality reduction and a plot showing varying accuracies over reduced dimensions.

Experiments to follow:
https://github.com/NeuroDataDesign/team-forbidden-forest/blob/master/Parimal%20Joshi/Final_pr_2.ipynb

[bdpedigo]

This issue is unclear about what you are actually proposing to PR into sklearn. You can be a lot more detailed about the fact that you are proposing a new tutorial, what the figures will be, what the data is

[parimal173]

This issue is unclear about what you are actually proposing to PR into sklearn. You can be a lot more detailed about the fact that you are proposing a new tutorial, what the figures will be, what the data is:

I have made changes in the issue, please tell me if its enough