Linear Algebra Methods
- Matrix factorization methods
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Principal Components Analysis
# evaluate pca with logistic regression algorithm for classification from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.linear_model import LogisticRegression # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, random_state=7) # define the pipeline steps = [('pca', PCA(n_components=10)), ('m', LogisticRegression())] model = Pipeline(steps=steps) # evaluate model cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) # report performance print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores))) -
Singular Value Decomposition
# evaluate svd with logistic regression algorithm for classification from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.pipeline import Pipeline from sklearn.decomposition import TruncatedSVD from sklearn.linear_model import LogisticRegression # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, random_state=7) # define the pipeline steps = [('svd', TruncatedSVD(n_components=10)), ('m', LogisticRegression())] model = Pipeline(steps=steps) # evaluate model cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) # report performance print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores))) - Non-Negative Matrix Factorization
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Linear Discriminant Analysis
# evaluate lda with logistic regression algorithm for classification from numpy import mean from numpy import std from sklearn.datasets import make_classification from sklearn.model_selection import cross_val_score from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.pipeline import Pipeline from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.linear_model import LogisticRegression # define dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, random_state=7) # define the pipeline steps = [('lda', LinearDiscriminantAnalysis(n_components=1)), ('m', LogisticRegression())] model = Pipeline(steps=steps) # evaluate model cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) # report performance print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))
Manifold Learning Methods
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Isomap Embedding
# define the pipeline steps = [('iso', Isomap(n_components=10)), ('m', LogisticRegression())] model = Pipeline(steps=steps) -
Locally Linear Embedding
# define the pipeline steps = [(‘lle’, LocallyLinearEmbedding(n_components=10)), (‘m’, LogisticRegression())] model = Pipeline(steps=steps)
- Multidimensional Scaling
- Spectral Embedding
- t-distributed Stochastic Neighbor Embedding
