Hello, in this article, we continue the topic Unsupervised Learning.
Read the previous post before this post.
Dimension reduction finds patterns in data, and uses these patterns to re-express it in a compressed form. This makes subsequent computation with the data much more efficient and this can be a big deal in a world of big dataset.
Principal Component Analysis (PCA)
PCA performs dimension reduction in two steps, and the first one, called “de-correlation” , doesn’t change the dimension of the data at all.
You can access the entire linked code above.
import matplotlib.pyplot as plt from scipy.stats import pearsonr width = grains[:,0] # Assign the 1st column of grains: length length = grains[:,1] # Scatter plot width vs length plt.scatter(width,length) plt.axis('equal') plt.show() # Calculate the Pearson correlation correlation, pvalue = pearsonr(width,length) # Display the correlation print(correlation)
from sklearn.decomposition import PCA # Create PCA instance: model model = PCA() # Apply the fit_transform method of model to grains: pca_features pca_features = model.fit_transform(grains) # Assign 0th column of pca_features: xs xs = pca_features[:,0] # Assign 1st column of pca_features: ys ys = pca_features[:,1] # Scatter plot xs vs ys plt.scatter(xs, ys) plt.axis('equal') plt.show() # Calculate the Pearson correlation of xs and ys correlation, pvalue = pearsonr(xs, ys) # Display the correlation print(correlation)
See you in the next article
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