How to do PCA with Dask?

How to do PCA with Dask?

How to do PCA with Dask?

This recipe helps you do PCA with Dask


Recipe Objective

How to do PCA with Dask?

PCA stands for **principal component Analysis**. It is used to reduce the dimensionality of a model using SVD to project in the lower dimensional data.

This algorithm depends on the size of the input data, SVD can be much more memory efficient than a PCA, and it allows sparse input as well. This algorithm has constant memory complexity.

#!pip install dask_ml #!pip install dask distributed --upgrade

Step 1- Importing Libraries.

Importing PCA from dask_ml.decomposition along with other libraries.

import numpy as np import dask.array as da from dask_ml.decomposition import PCA

Step 2- Creating arrays.

We will create multi dimensional array.

x = np.array([[1, -6], [2, -5], [3, -4], [4, -3], [5, -2], [6, -1]]) X = da.from_array(x, chunks=x.shape)

Step 3- Applying PCA to the arrays.

We will reduce the features by applying PCA to the arrays.

pca = PCA(n_components=2)

Step 4- Printing explained variance ratio.

We will print the explained variance ratio to better understand the model working.


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