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Results tagged with pca
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user 294318
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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PCA and the train/test/validation split
I want to apply the methodology described by cbeleites unhappy with SX in PCA and the train/test split. … = PCA(i, svd_solver='arpack')
scaler= StandardScaler()
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
fScaler …
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