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Results tagged with pca
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user 305322
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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How to interpret data not separated by PCA but by T-sne/UMAP
I have a classification problem, to have a first look at my data I do a PCA followed by TSNE and UMAP.
My clusters are nicely separated by TSNE and UMAP but not by PCA. …
- 37
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Linear model performs better on non-linear classification
I'm working on the following dataset :
https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq
I start by looking at PCA/TSNE/UMAP to have a first sight, on all the data using the following … code :
# Prepare plots
fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(20,8))
# remove sample name and scale
#df = df.drop('Unnamed: 0', axis=1)
x = StandardScaler().fit_transform(df)
# PCA
pca = PCA …
- 37