pca = PCA(n_component=2) X_train = pca.fit_transform(X_train) X_test = pca.transform(X_test) variance_explained = pca.explained_variance_ratio_ #Apply LDA (Linear Separation) from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA lda = LDA(n_components=2) X_train = lda.fit_transform(X_train, y_train) X_test = lda.transform(X_test) from sklearn.discrimnant_analysis import LinearDiscriminantAnalysis as LDA lda = LDA(n_components=2) X_train = lda.fit_tranform(X_train, y_train) X_test = lda.transform(X_test) #Apply Kernel PCA (Non separable dataset) from sklearn.decomposition import KernelPCA kpca = KernelPCA(n_components=2, kernel='rbf') X_train = kpca.fit_transform(X_train) X_test = kpca.transform(X_test) #Feature Extraction #PCA principal componant analysis from sklearn.decomposition import PCA pca = PCA(n_component=2) X_train = pca.fit_transform(X_train)
