一、原理部分：

依然是圖片~

二、sklearn實現：

import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import seaborn as sns mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False from sklearn.datasets import load_digits from sklearn.svm import SVC from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler#載入數據 digits = load_digits() x_data = digits.data y_data = digits.target x_train,x_test,y_train,y_test = train_test_split(x_data,y_data) st = StandardScaler() x_train_st = st.fit_transform(x_train) x_test_st = st.fit_transform(x_test)#訓練模型 svc = SVC(kernel='rbf') model_svc = GridSearchCV(svc,param_grid=({'C':[7,8,9],'gamma':[0.01,0.02]})) model_svc.fit(x_train_st,y_train) print(model_svc.best_params_) print(model_svc.best_score_) y_hat = model_svc.predict(x_test_st) print(accuracy_score(y_hat,y_test))

{'gamma': 0.01, 'C': 7}
0.978470675575
0.982222222222

感覺SVM調參要很注意，如果過低說明你很可能過擬合了！可以先給一個比較大范圍的，然后逐步縮小

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轉載于:https://www.cnblogs.com/slowlyslowly/p/8824367.html

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