09 scikit-learn中的回歸問題 import numpy as np import matplotlib.pyplot as plt from sklearn import datasets import datetime;print("Run by CYJ,",datetime.datetime.now()) Run by CYJ, 2022-01-20 20:29:12.409480 boston = datasets.load_boston()X = boston.data y = boston.targetX = X[y < 50.0] y = y[y < 50.0] X.shape (490, 13) from playML.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, seed=666) scikit-learn中的線性回歸 from sklearn.linear_model import LinearRegressionlin_reg = LinearRegression() lin_reg.fit(X_train, y_train) LinearRegression() lin_reg.coef_ array([-1.20354261e-01, 3.64423279e-02, -3.61493155e-02, 5.12978140e-02,-1.15775825e+01, 3.42740062e+00, -2.32311760e-02, -1.19487594e+00,2.60101728e-01, -1.40219119e-02, -8.35430488e-01, 7.80472852e-03,-3.80923751e-01]) lin_reg.intercept_ 34.117399723229845 lin_reg.score(X_test, y_test) 0.8129794056212809 kNN Regressor from sklearn.preprocessing import StandardScalerstandardScaler = StandardScaler() standardScaler.fit(X_train, y_train) X_train_standard = standardScaler.transform(X_train) X_test_standard = standardScaler.transform(X_test) from sklearn.neighbors import KNeighborsRegressorknn_reg = KNeighborsRegressor() knn_reg.fit(X_train_standard, y_train) knn_reg.score(X_test_standard, y_test) 0.847923904906593 from sklearn.model_selection import GridSearchCVparam_grid = [{"weights": ["uniform"],"n_neighbors": [i for i in range(1, 11)]},{"weights": ["distance"],"n_neighbors": [i for i in range(1, 11)],"p": [i for i in range(1,6)]} ]knn_reg = KNeighborsRegressor() grid_search = GridSearchCV(knn_reg, param_grid, n_jobs=-1, verbose=1) grid_search.fit(X_train_standard, y_train) Fitting 5 folds for each of 60 candidates, totalling 300 fits GridSearchCV(estimator=KNeighborsRegressor(), n_jobs=-1,param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],'weights': ['uniform']},{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],'p': [1, 2, 3, 4, 5], 'weights': ['distance']}],verbose=1) grid_search.best_params_ {'n_neighbors': 7, 'p': 1, 'weights': 'distance'} grid_search.best_score_ 0.8121986929882669 grid_search.best_estimator_.score(X_test_standard, y_test) 0.8703184399069476

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