Kernel_Ridge_Regression_VS_Ridge_Jyoti_R_Sahoo.html

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Name - Jyoti Ranjan Sahoo RUID - 178006691 Stats & Machine Learning HW- 7 kernel ridge regression & Regular Ridge regression In [1]: import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.cross_validation import train_test_split import matplotlib.pyplot as plt from sklearn.metrics import mean_squared_error, r2_score C:\Users\jyo\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning) Load the dataset In [2]: from sklearn.datasets import load_boston boston= load_boston() boston Out[2]: {'DESCR': "Boston House Prices dataset\n===========================\n\nNotes\n------\nData Set Characteristics: \n\n :Number of Instances: 506 \n\n :Number of Attributes: 13 numeric/categorical predictive\n \n :Median Value (attribute 14) is usually the target\n\n :Attribute Information (in order):\n - CRIM per capita crime rate by town\n - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n - INDUS proportion of non-retail business acres per
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town\n - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n - NOX nitric oxides concentration (parts per 10 million)\n - RM average number of rooms per dwelling\n - AGE proportion of owner-occupied units built prior to 1940\n - DIS weighted distances to five Boston employment centres\n - RAD index of accessibility to radial highways\n - TAX full-value property-tax rate per $10,000\n - PTRATIO pupil-teacher ratio by town\n - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n - LSTAT % lower status of the population\n - MEDV Median value of owner-occupied homes in $1000's\n\n
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