支持向量机的性别识别问题

我最近一直在使用SVM在一个性别识别项目（python）中工作，但是我迷失了获得的结果：

为什么召回男性为0.09，女性为1？还是女性的精确度只有50％？

SVM Accuracy: 0.540041067762
Classification report:
           precision   recall  f1-score   support
Females       0.52      1.00      0.68       242
Males         1.00      0.09      0.16       245

avg / total       0.76      0.54      0.42       487

我将所有图像裁剪，对齐并呈灰度显示如何提高精度？

我需要更改以下参数：clf = svm.SVC（）？

我的代码：

import os, sys
import numpy as np
import PIL.Image as Image
import cv2
from sklearn import svm
from sklearn.externals import joblib
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score


def read_images(path, id, sz=None):
    c = id
    X,y = [], []
    for dirname, dirnames, filenames in os.walk(path):
        for subdirname in dirnames:
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                try:
                    im = Image.open(os.path.join(subject_path, filename))
                    im = im.convert("L")
                    # resize to given size (if given)
                    if (sz is not None):
                        im = im.resize(sz, Image.ANTIALIAS)
                    X.append(np.asarray(im, dtype=np.uint8).ravel())
                    y.append(c)
                except IOError as e:
                    print "I/O error({0}): {1}".format(e.errno, e.strerror)
                except:
                    print "Unexpected error:", sys.exc_info()[0]
                    raise
                        #c = c+1

    return [X,y]


def main():

    contador = 0;

    # check arguments
    if len(sys.argv) != 3:
        print "USAGE: example.py </path/to/images/males> </path/to/images/females>"
        sys.exit()

    # read images and put them into Vectors and id's
    [X,x] = read_images(sys.argv[1], 1)
    [Y, y] = read_images(sys.argv[2], -1)

    # R all images and r all id's
    [R, r] = [X+Y, x+y]
    R_train, R_test, r_train, r_test = train_test_split(R, r)

    # Default svm
    clf = svm.SVC()   

    clf.fit(R_train, r_train)

    r_pred = clf.predict(R_test)

    target_names = ['Female', 'Male']
    print "SVM Accuracy:", accuracy_score(r_test, r_pred)
    print "Classification report:\n", classification_report(r_test, r_pred, target_names=target_names) 




if __name__ == '__main__':
    main()

我将非常感谢您如何使用SVM进行性别识别方面的帮助，感谢您的阅读

好的，现在使用归一化的数据（介于0和1之间），我尝试使用GridSearchCV来使用以下方法调整C和Gamma：

    param_grid = [
  {'C': [1, 10, 100, 1000], 'kernel': ['linear']},
  {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
 ]
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, param_grid)

一段时间后，我得到这个结果：

{'n_jobs': 1, 'verbose': 0, 'estimator__gamma': 0.0, 'estimator__probability': False, 'param_grid': [{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}, {'kernel': ['rbf'], 'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}], 'cv': None, 'scoring': None, 'estimator__cache_size': 200, 'estimator__verbose': False, 'pre_dispatch': '2*n_jobs', 'estimator__kernel': 'rbf', 'fit_params': {}, 'estimator__max_iter': -1, 'refit': True, 'iid': True, 'estimator__shrinking': True, 'score_func': None, 'estimator__degree': 3, 'estimator__class_weight': None, 'loss_func': None, 'estimator__random_state': None, 'estimator': SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False), 'estimator__coef0': 0.0, 'error_score': 'raise', 'estimator__tol': 0.001, 'estimator__C': 1.0}

SVM Accuracy: 0.767326732673
Classification report:
             precision    recall  f1-score   support

    Females       0.76      0.77      0.76       294
    Males         0.78      0.77      0.77       312

avg / total       0.77      0.77      0.77       606

我不知道这是使用它的正确方法，并且给出此结果的参数C和gamma是什么，但是分类器已经改进，有人可以告诉我我是否做得很好，结果又意味着什么，或者我怎么能做这个？谢谢