# 人工智慧監督學習(回歸)

## 在Python中構建回歸器

``````import numpy as np
from sklearn import linear_model
import sklearn.metrics as sm
import matplotlib.pyplot as plt
``````

``````input = 'D:/ProgramData/linear.txt'
``````

``````input_data = np.loadtxt(input, delimiter=',')
X, y = input_data[:, :-1], input_data[:, -1]
``````

``````training_samples = int(0.6 * len(X))
testing_samples = len(X) - num_training

X_train, y_train = X[:training_samples], y[:training_samples]

X_test, y_test = X[training_samples:], y[training_samples:]
``````

``````reg_linear = linear_model.LinearRegression()
``````

``````reg_linear.fit(X_train, y_train)
``````

``````y_test_pred = reg_linear.predict(X_test)
``````

``````plt.scatter(X_test, y_test, color = 'red')
plt.plot(X_test, y_test_pred, color = 'black', linewidth = 2)
plt.xticks(())
plt.yticks(())
plt.show()
``````

``````print("Performance of Linear regressor:")
print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2))
print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2))
print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2))
print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred),
2))
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))
``````

``````Mean absolute error = 1.78
Mean squared error = 3.89
Median absolute error = 2.01
Explain variance score = -0.09
R2 score = -0.09
``````

``````2,4.82.9,4.72.5,53.2,5.56,57.6,43.2,0.92.9,1.92.4,
3.50.5,3.41,40.9,5.91.2,2.583.2,5.65.1,1.54.5,
1.22.3,6.32.1,2.8
``````

``````import numpy as np
from sklearn import linear_model
import sklearn.metrics as sm
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
``````

``````input = 'D:/ProgramData/Mul_linear.txt'
``````

``````input_data = np.loadtxt(input, delimiter=',')
X, y = input_data[:, :-1], input_data[:, -1]
``````

``````training_samples = int(0.6 * len(X))
testing_samples = len(X) - num_training

X_train, y_train = X[:training_samples], y[:training_samples]

X_test, y_test = X[training_samples:], y[training_samples:]
``````

``````reg_linear_mul = linear_model.LinearRegression()
``````

``````reg_linear_mul.fit(X_train, y_train)
``````

``````y_test_pred = reg_linear_mul.predict(X_test)

print("Performance of Linear regressor:")
print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2))
print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2))
print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2))
print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2))
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))
``````

``````Mean absolute error = 0.6
Mean squared error = 0.65
Median absolute error = 0.41
Explain variance score = 0.34
R2 score = 0.33
``````

``````polynomial = PolynomialFeatures(degree = 10)
X_train_transformed = polynomial.fit_transform(X_train)
datapoint = [[2.23, 1.35, 1.12]]
poly_datapoint = polynomial.fit_transform(datapoint)

poly_linear_model = linear_model.LinearRegression()
poly_linear_model.fit(X_train_transformed, y_train)
print("\nLinear regression:\n", reg_linear_mul.predict(datapoint))
print("\nPolynomial regression:\n", poly_linear_model.predict(poly_datapoint))
``````

``````[2.40170462]
``````

``````[1.8697225]
``````

``````2,4.8,1.2,3.22.9,4.7,1.5,3.62.5,5,2.8,23.2,5.5,3.5,2.16,5,
2,3.27.6,4,1.2,3.23.2,0.9,2.3,1.42.9,1.9,2.3,1.22.4,3.5,
2.8,3.60.5,3.4,1.8,2.91,4,3,2.50.9,5.9,5.6,0.81.2,2.58,
3.45,1.233.2,5.6,2,3.25.1,1.5,1.2,1.34.5,1.2,4.1,2.32.3,
6.3,2.5,3.22.1,2.8,1.2,3.6
``````