The final training set has 46 columns, including dummy variables. The model above uses every one...

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The final training set has 46 columns, including dummy variables. The model above uses every one of the features in the model. Your next task will be to reduce this dimensionality. Use PCA to reduce the dimensionality of x_train_final. • Fit a PCA instance with X_train_final with random_state=24 (leave the rest as default) and assign to pca_all. • Transform X_train_final using the fit pca_all instance. Assign the result as a numpy array to pca_components. [ ]: ### GRADED from sklearn.decomposition import PCA ### YOUR SOLUTION HERE pca_all= None ### ### YOUR CODE HERE ### The final training set has 46 columns, including dummy variables. The model above uses every one of the features in the model. Your next task will be to reduce this dimensionality. Use PCA to reduce the dimensionality of x_train_final. • Fit a PCA instance with X_train_final with random_state=24 (leave the rest as default) and assign to pca_all. • Transform X_train_final using the fit pca_all instance. Assign the result as a numpy array to pca_components. [ ]: ### GRADED from sklearn.decomposition import PCA ### YOUR SOLUTION HERE pca_all= None ### ### YOUR CODE HERE ###

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Use the following code to fit a PCA instance and transform ... View the full answer