Delete 3217-classification-lr-example4.py

3217-classification-lr-example4.py

-
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn import datasets, linear_model
-from sklearn.metrics import mean_squared_error, r2_score
-
-# Load the diabetes dataset
-diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
-
-print (diabetes_X.shape)
-# Use only one feature
-feature_to_use = 2
-diabetes_X = diabetes_X[:, np.newaxis, feature_to_use]
-print (diabetes_X.shape)
-
-test_samples = 20
-
-# Split the data into training/testing sets
-diabetes_X_train = diabetes_X[:-test_samples]
-diabetes_X_test = diabetes_X[-test_samples:]
-
-# Split the targets into training/testing sets
-diabetes_y_train = diabetes_y[:-test_samples]
-diabetes_y_test = diabetes_y[-test_samples:]
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-
-
-
-# Create linear regression object
-regr = linear_model.LinearRegression()
-
-
-# Train the model using the training sets
-regr.fit(diabetes_X_train, diabetes_y_train)
-
-# Make prediction using the testing set
-diabetes_y_pred = regr.predict(diabetes_X_test)
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-print (diabetes_y_train.shape)
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-print (diabetes_y_test.shape)
-
-# The coefficients
-print("Coefficients: \n", regr.coef_)
-# The mean squared error
-print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
-# The coefficient of determination: 1 is perfect prediction
-print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
-
-# Plot outputs
-plt.scatter(diabetes_X_test, diabetes_y_test, color="black") #grond truth actual test labels
-plt.scatter(diabetes_X_test, diabetes_y_pred, color="red")  #predicted   test labels
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-plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)#predicted   test labels
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-plt.xticks(())
-plt.yticks(())
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-plt.show()