github 地址： https://github.com/HansRen1024/Image-Pre-Classification/tree/master
之前训练 SVM 用了 PSO 太慢了。
这次比较幸运看到一篇关于调参的博客。
给了很大启发。
地址： http://blog.csdn.net/han_xiaoyang/article/details/52663170
不具体针对某种分类算法详细说了，这个真的需要大量实践经验，这也是我欠缺的。
我参考上面博客做了一些实验，准确率是在逐步提升，但因为我特征处理这块做的不好，准确率提升不明显。
再一点，我没用文章中作者使用的调优接口 GridSearchCV，我是单纯的使用 for 循环来做的。
我的代码中只有 adaboost 的函数，其他方法可以参考我 上一篇博客
自己修改。
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 23 11:24:32 2018
@author: hans
"""
from sklearn.externals import joblib
from sklearn.ensemble import AdaBoostClassifier
from sklearn import tree
filename = '_feature_rgb.pkl'
train_list = "train_all.txt"
test_list = "test_all.txt"
def adaboost(n):
clf = AdaBoostClassifier(tree.DecisionTreeClassifier(criterion='gini',max_depth=11, min_samples_split=400, \
min_samples_leaf=30,max_features=30,random_state=10), \
algorithm="SAMME", n_estimators=n, learning_rate=0.001,random_state=10)
return clf
def findBestParam():
X_train = joblib.load(train_list.split('.')[0]+filename)
y_train = joblib.load(train_list.split('.')[0]+'_label.pkl')
X_test = joblib.load(test_list.split('.')[0]+filename)
y_test = joblib.load(test_list.split('.')[0]+'_label.pkl')
best_test_score=0
best_train_score=0
best_param=0
for n in range(1500,1501,10):
clf = adaboost(n)
clf = clf.fit(X_train, y_train)
train_score = clf.score(X_train, y_train)
test_score = clf.score(X_test, y_test)
print ("--------------------------------\nCurrent train score: %.4f" %train_score)
print ("Current test score: %.4f" %test_score)
print ("Current param: %d" %n)
if test_score > best_test_score:
best_test_score = test_score
best_train_score = train_score
best_param = n
print ("--------------------------------\nBest train score: %.4f" %best_train_score)
print ("Best test score: %.4f" %best_test_score)
print ("Best param: %d" %best_param)
if __name__ == '__main__':
findBestParam()