sklearn学习一
转发说明:by majunman from HIT email:2192483210@qq.com
简介:scikit-learn是数据挖掘和数据分析的有效工具,它建立在 NumPy, SciPy, and matplotlib基础上。开源的但商业不允许
1. Supervised learning
1.1. Generalized Linear Models
1.1.1. Ordinary Least Squares最小二乘法
>>> from sklearn import linear_model >>> reg = linear_model.LinearRegression() >>> reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) >>> reg.coef_ array([ 0.5, 0.5])
reg-http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression
reg.coef_ 是回归函数的结果,即相关系数
具体实验:
print(__doc__) # Code source: Jaques Grobler # License: BSD 3 clause 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 = datasets.load_diabetes() #加载diabetes数据集(sklearn提供的几种数据集之一,该数据是糖尿病数据集) # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] #只加载一个特征值 # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(diabetes_X_train, diabetes_y_train) # Make predictions using the testing set diabetes_y_pred = regr.predict(diabetes_X_test) # 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)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred)) # Plot outputs plt.scatter(diabetes_X_test, diabetes_y_test, color='black') plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3) plt.xticks(()) plt.yticks(()) plt.show()