Kernel: Python 2 (SageMath)
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%pylab inline from sklearn import linear_model figure(num=None, figsize=(32, 24), dpi=320, facecolor='w', edgecolor='k') from pylab import rcParams rcParams['figure.figsize'] = 10, 10
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Populating the interactive namespace from numpy and matplotlib
<matplotlib.figure.Figure at 0x7fe55d663450>
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# Split the data into training/testing sets diabetes_X_train = np.array([[20], [30], [21], [31], [22], [25], [29]]) diabetes_X_test = np.array([[10], [35]]) # Split the targets into training/testing sets diabetes_y_train = [2, 4, 2, 4, 4, 4.5, 5] # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(diabetes_X_train, diabetes_y_train) # The coefficients print('Coefficients: \n', regr.coef_) # The mean squared error print("Mean squared error: %.2f" % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test)) # Plot outputs #plt.scatter(diabetes_X_train, diabetes_y_train, color='black') plt.scatter(diabetes_X_train[0], diabetes_y_train[0], color='black', label='Conventional, outdoor farms (non-local)') plt.scatter(diabetes_X_train[1], diabetes_y_train[1], color='red', label='Organic, outdoor farms (non-local)') plt.scatter(diabetes_X_train[2], diabetes_y_train[2], color='green', label='Conventional, outdoor farms (local)') plt.scatter(diabetes_X_train[3], diabetes_y_train[3], color='blue', label='Organic, outdoor farms (local)') plt.scatter(diabetes_X_train[4], diabetes_y_train[4], color='yellow', label='Hydroponic greenhouse farms ') plt.scatter(diabetes_X_train[5], diabetes_y_train[5], color='cyan', label='Indoor and vertical farms ') plt.scatter(diabetes_X_train[6], diabetes_y_train[6], color='magenta', label='Robotany ') plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue', linewidth=3) plt.title('Price-Quality Chart') plt.legend(loc='lower right') plt.axis('on') plt.xlabel('Pricing per pound') plt.ylabel('Quality (0-5)') plt.axis([0, 60, 0, 6]) plt.show()
Out[95]:
('Coefficients: \n', array([ 0.18352273]))
Mean squared error: 5.80
Variance score: -22.18
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# Plot outputs #plt.scatter(diabetes_X_train, diabetes_y_train, color='black') plt.scatter(diabetes_X_train[0], diabetes_y_train[0], color='black', label='Conventional, outdoor farms (non-local)') plt.scatter(diabetes_X_train[1], diabetes_y_train[1], color='red', label='Organic, outdoor farms (non-local)') plt.scatter(diabetes_X_train[2], diabetes_y_train[2], color='green', label='Conventional, outdoor farms (local)') plt.scatter(diabetes_X_train[3], diabetes_y_train[3], color='blue', label='Organic, outdoor farms (local)') plt.scatter(diabetes_X_train[4], diabetes_y_train[4], color='yellow', label='Hydroponic greenhouse farms ') plt.scatter(diabetes_X_train[5], diabetes_y_train[5], color='cyan', label='Indoor and vertical farms ') plt.scatter(diabetes_X_train[6], diabetes_y_train[6], color='magenta', label='Robotany ') plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue', linewidth=3) plt.axis([10, 35, 0, 5]) plt.show()
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