We all know that SVM can achieve a good classification effect if the parameters are better adjusted, but SVM does have more parameters, such as the ones introduced here:
https://blog.csdn.net/xiaodongxiexie/article/details/70667101
Some friends also explained the parameter adjustment process in more detail:
https://blog.csdn.net/baidu_15113429/article/details/72673466
According to netizens, the kernel, C and gamma should be mainly adjusted in the process of SVM parameter adjustment. For SKLearn, we can use GridSearchCV to automatically search the parameters. Some netizens have introduced its use method in detail:
https://blog.csdn.net/cherdw/article/details/54970366
The documentation on its official website is here: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
However, the above blogs and tutorials are a bit complicated. I will give the simplest example here:
from sklearn import svm
from sklearn.model_selection import GridSearchCV
svr = svm.SVC()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 2, 4], 'gamma':[0.125, 0.25, 0.5 ,1, 2, 4]}
clf = GridSearchCV(svr, parameters, scoring='f1')
clf.fit(X, y)
print('The parameters of the best model are: ')
print(clf.best_params_)
About scoring this parameter, there are more introductions here:
http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter
I use f1 here, other parameter values can refer to this table:
| Scoring | Function | Comment |
|---|---|---|
| Classification | ||
| ‘accuracy’ | metrics.accuracy_score |
|
| ‘average_precision’ | metrics.average_precision_score |
|
| 'f1' | metrics.f1_score |
for binary targets |
| ‘f1_micro’ | metrics.f1_score |
micro-averaged |
| ‘f1_macro’ | metrics.f1_score |
macro-averaged |
| ‘f1_weighted’ | metrics.f1_score |
weighted average |
| ‘f1_samples’ | metrics.f1_score |
by multilabel sample |
| ‘neg_log_loss’ | metrics.log_loss |
requires predict_proba support |
| ‘precision’ etc. | metrics.precision_score |
suffixes apply as with ‘f1’ |
| ‘recall’ etc. | metrics.recall_score |
suffixes apply as with ‘f1’ |
| ‘roc_auc’ | metrics.roc_auc_score |
|
| Clustering | ||
| ‘adjusted_mutual_info_score’ | metrics.adjusted_mutual_info_score |
|
| ‘adjusted_rand_score’ | metrics.adjusted_rand_score |
|
| ‘completeness_score’ | metrics.completeness_score |
|
| ‘fowlkes_mallows_score’ | metrics.fowlkes_mallows_score |
|
| ‘homogeneity_score’ | metrics.homogeneity_score |
|
| ‘mutual_info_score’ | metrics.mutual_info_score |
|
| ‘normalized_mutual_info_score’ | metrics.normalized_mutual_info_score |
|
| ‘v_measure_score’ | metrics.v_measure_score |
|
| Regression | ||
| ‘explained_variance’ | metrics.explained_variance_score |
|
| ‘neg_mean_absolute_error’ | metrics.mean_absolute_error |
|
| ‘neg_mean_squared_error’ | metrics.mean_squared_error |
|
| ‘neg_mean_squared_log_error’ | metrics.mean_squared_log_error |
|
| ‘neg_median_absolute_error’ | metrics.median_absolute_error |
|
| 'r2' | metrics.r2_score |