【转】http://blog.csdn.net/chenglibin1988/article/details/6769634

查准率和查全率是信息检索效率评价的两个定量指标，不仅可以用来评价每次检索的准确性和全面性，也是在信息检索系统评价中衡量系统检索性能的重要方面。

查准率(Precision

ratio，简称为P)，是指检出的相关文献数占检出文献总数的百分比。查准率反映检索准确性，其补数就是误检率。

查全率(Recall

ratio，简称为R)，是指检出的相关文献数占系统中相关文献总数的百分比。查全率反映检索全面性，其补数就是漏检率。

查全率＝(检索出的相关信息量/系统中的相关信息总量)*100%

查准率＝(检索出的相关信息量/检索出的信息总量)*100%

前者是衡量检索系统和检索者检出相关信息的能力，后者是衡量检索系统和检索者拒绝非相关信息的能力。两者合起来，即表示检索效率。

利用查准率和查全率指标，可以对每一次检索进行检索效率的评价，为检索的改进调整提供依据。利用这两个量化指标，也可以对信息检索系统的性能水平进行评

价。要评价信息检索系统的性能水平，就必须在一个检索系统中进行多次检索。每进行一次检索，都计算其查准率和查全率，并以此作为坐标值，在平面坐标图上标

示出来。通过大量的检索，就可以得到检索系统的性能曲线。实验证明，在查全率和查准率之间存在着相反的相互依赖关系–如果提高输出的查全率，就会降低其查

准率，反之亦然。

网上源码有很多，这里找到了一个是Stefan Schroedl写的，跟大家分享一下：

function [prec, tpr, fpr, thresh] = prec_rec(score, target,

varargin)

% PREC_REC - Compute and plot precision/recall and ROC curves.

%

%  PREC_REC(SCORE,TARGET), where SCORE and TARGET

are equal-sized vectors,

%  and TARGET is binary, plots the corresponding

precision-recall graph

%  and the ROC curve.

%

%  Several options of the form

PREC_REC(...,'OPTION_NAME', OPTION_VALUE)

%  can be used to modify the default

behavior.

%  -

'instanceCount': Usually it is assumed that one line in the

input

%  data

corresponds to a single sample. However, it

%  might be

the case that there are a total of N

%  instances

with the same SCORE, out of which

%  TARGET are

classified as positive, and (N -

%  TARGET)

are classified as negative. Instead of

%  using

repeated samples with the same SCORE, we

%  can

summarize these observations by means of this

%  option.

Thus it requires a vector of the same

%  size as

TARGET.

%  -

'numThresh'  : Specify the

(maximum) number of score intervals.

%  Generally,

splits are made such that each

%  interval

contains about the same number of sample

%  lines.

%  -

'holdFigure'  : [0,1] draw into the current

figure, instead of

%  creating a

new one.

%  - 'style'

: Style specification for plot command.

%  -

'plotROC'  :

[0,1] Explicitly specify if ROC curve should be

%  plotted.

%  -

'plotPR'  :

[0,1] Explicitly specify if precision-recall curve

%  should be

plotted.

%  -

'plotBaseline' : [0,1] Plot a baseline of the random

classifier.

%

%  By default, when output arguments are

specified, as in

%  [PREC, TPR, FPR, THRESH] = PREC_REC(...),

%  no plot is generated. The arguments are the

score thresholds, along

%  with the respective precisions, true-positive,

and false-positive

%  rates.

%

%  Example:

%

% x1 = rand(1000, 1);

% y1 = round(x1 + 0.5*(rand(1000,1) - 0.5));

% prec_rec(x1, y1);

% x2 = rand(1000,1);

% y2 = round(x2 + 0.75 * (rand(1000,1)-0.5));

% prec_rec(x2, y2, 'holdFigure', 1);

% legend('baseline','x1/y1','x2/y2','Location','SouthEast');

% Copyright @ 9/22/2010 Stefan Schroedl

% Updated  3/16/2010

optargin = size(varargin, 2);

stdargin = nargin - optargin;

if stdargin < 2

error('at least 2 arguments required');

end

% parse optional arguments

num_thresh = -1;

hold_fig = 0;

plot_roc = (nargout <= 0);

plot_pr  = (nargout <= 0);

instance_count = -1;

style = '';

plot_baseline = 1;

i = 1;

while (i <= optargin)

if (strcmp(varargin{i}, 'numThresh'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

num_thresh = varargin{i+1};

i = i + 2;

end

elseif (strcmp(varargin{i}, 'style'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

style = varargin{i+1};

i = i + 2;

end

elseif (strcmp(varargin{i}, 'instanceCount'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

instance_count = varargin{i+1};

i = i + 2;

end

elseif (strcmp(varargin{i}, 'holdFigure'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

if ~isempty(get(0,'CurrentFigure'))

hold_fig = varargin{i+1};

end

i = i + 2;

end

elseif (strcmp(varargin{i}, 'plotROC'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

plot_roc = varargin{i+1};

i = i + 2;

end

elseif (strcmp(varargin{i}, 'plotPR'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

plot_pr = varargin{i+1};

i = i + 2;

end

elseif (strcmp(varargin{i}, 'plotBaseline'))

if (i >= optargin)

error('argument required for %s',

varargin{i});

else

plot_baseline = varargin{i+1};

i = i + 2;

end

elseif (~ischar(varargin{i}))

error('only two numeric arguments required');

else

error('unknown option: %s', varargin{i});

end

end

[nx,ny]=size(score);

if (nx~=1 && ny~=1)

error('first argument must be a vector');

end

[mx,my]=size(target);

if (mx~=1 && my~=1)

error('second argument must be a vector');

end

score  =  score(:);

target = target(:);

if (length(target) ~= length(score))

error('score and target must have same

length');

end

if (instance_count == -1)

% set default for total instances

instance_count = ones(length(score),1);

target = max(min(target(:),1),0); % ensure binary

target

else

if numel(instance_count)==1

% scalar

instance_count = instance_count *

ones(length(target), 1);

end

[px,py] = size(instance_count);

if (px~=1 && py~=1)

error('instance count must be a vector');

end

instance_count = instance_count(:);

if (length(target) ~= length(instance_count))

error('instance count must have same length as

target');

end

target = min(instance_count, target);

end

if num_thresh < 0

% set default for number of thresholds

score_uniq = unique(score);

num_thresh = min(length(score_uniq), 100);

end

qvals = (1:(num_thresh-1))/num_thresh;

thresh = [min(score) quantile(score,qvals)];

% remove identical bins

thresh = sort(unique(thresh),2,'descend');

total_target = sum(target);

total_neg = sum(instance_count - target);

prec = zeros(length(thresh),1);

tpr  = zeros(length(thresh),1);

fpr  = zeros(length(thresh),1);

for i = 1:length(thresh)

idx  =

(score >= thresh(i));

fpr(i)  =

sum(instance_count(idx) - target(idx));

tpr(i)  = sum(target(idx)) /

total_target;

prec(i) = sum(target(idx)) /

sum(instance_count(idx));

end

fpr = fpr / total_neg;

if (plot_pr || plot_roc)

% draw

if (~hold_fig)

figure

if (plot_pr)

if (plot_roc)

subplot(1,2,1);

end

if (plot_baseline)

target_ratio = total_target / (total_target +

total_neg);

plot([0 1], [target_ratio target_ratio],

'k');

end

hold on

hold all

plot([0; tpr], [1 ; prec], style); % add pseudo

point to complete curve

xlabel('recall');

ylabel('precision');

title('precision-recall graph');

end

if (plot_roc)

if (plot_pr)

subplot(1,2,2);

end

if (plot_baseline)

plot([0 1], [0 1], 'k');

end

hold on;

hold all;

plot([0; fpr], [0; tpr], style); % add pseudo

point to complete curve

xlabel('false positive rate');

ylabel('true positive rate');

title('roc curve');

%axis([0 1 0 1]);

if (plot_roc && plot_pr)

% double the width

rect = get(gcf,'pos');

rect(3) = 2 * rect(3);

set(gcf,'pos',rect);

end

end

else

if (plot_pr)

if (plot_roc)

subplot(1,2,1);

end

plot([0; tpr],[1 ; prec], style); % add pseudo

point to complete curve

end

if (plot_roc)

if (plot_pr)

subplot(1,2,2);

end

plot([0; fpr], [0; tpr], style);

end

end

end