In [25]: #Get error of Standardization>PCA>Random Forest pipeline y_train) scale_pca_pipe_forest.fit(X_train_3, scale_pca_pipe_forest_test_predictions = scale_pca_pipe_forest.predict (X_test_3) mean_absolute_error(y_test, scale_pca_pipe_forest_test_predictions) NameError Input In [25], in () Traceback (most recent call last) 1 #Get error of Standardization>PCA>Random Forest pipeline ----> 2 scale_pca_pipe_forest. fit (x_train_3, y_train) 3 scale_pca_pipe_forest_test_predictions = scale_pca_pipe_forest.predict (x_test_3) 4 mean absolute_error(y_test, scale_pca_pipe_forest_test_predictions) NameError: name 'scale_pca_pipe_forest' is not defined

In [25]: #Get error of Standardization>PCA>Random Forest pipeline y_train) scale_pca_pipe_forest.fit(X_train_3, scale_pca_pipe_forest_test_predictions = scale_pca_pipe_forest.predict (X_test_3) mean_absolute_error(y_test, scale_pca_pipe_forest_test_predictions) NameError Input In [25], in () Traceback (most recent call last) 1 #Get error of Standardization>PCA>Random Forest pipeline ----> 2 scale_pca_pipe_forest. fit (x_train_3, y_train) 3 scale_pca_pipe_forest_test_predictions = scale_pca_pipe_forest.predict (x_test_3) 4 mean absolute_error(y_test, scale_pca_pipe_forest_test_predictions) NameError: name 'scale_pca_pipe_forest' is not defined

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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PYTHON/JUPYTER NOTEBOOKS given the attached fourier data : # Measurements of fourier data data = [ [-2.00, -9.37], [-1.00, 10.00], [-1.14, 10.83], [-0.29, -13.88], [0.00, -18.00], [0.57, -1.83], [1.00, 14.00], [1.43, 14.5], [2.00, 0.00], [2.29, -1.38], [3.00, 28.00], [3.14, 35.64], [4.00, 37.88], [5.00, -23.00], [4.86, -17.52], [5.71, -14.63], [6.00, -1.00], [6.50, -15.00], [6.57, -1.73], [7.00, 12.00], [7.43, 5.97], [7.50, 2.00], [8.29, 22.78], [9.00, 1.00], [9.14, -6.41], [10.0, -9.37] Fit the data with the linear least-squares fit method using Fourier basis functions. Use 15 pairs of sines and cosines and create the plots/results below. x_vec = [ 1.591 -0.42 5.684 -5.112 -2.257 0.36 4.546 -7.626 0.341 -4.391 1.092 1.878 4.286 7.783 -3.427 -4.608 1.763 -0.957 -1.751 3.441 2.857 -2.624 2.96 0.911 -2.538 2.782 -1.943 -8.819 1.635 2.123 2.123] Local Relative Error (discarding y values < 0.1): mean [%] = 17.08 std [%] = 89.43…
tuiwper
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