前言

吳恩達的課程堪稱經典，有必要總結一下。
學以致用，以學促用，通過筆記總結，鞏固學習成果，復習新學的概念。

目錄

文章目錄

• 前言
• 目錄
• 正文
• • 誤差分析
  • 不均勻數據的評估方法
  • 準確度和召回率的取舍
• 設計一個高準確度學習系統(tǒng)
• 作業(yè)習題
• • ex6.m
  • gaussiankernel.m
  • dataset3Params.m
  • processemial.m
  • emailFeatures.m

正文

本節(jié)主要介紹機器學習系統(tǒng)是如何設計的，并舉了一些例子。
垃圾郵件和非垃圾郵件的例子。
垃圾郵件分類器，監(jiān)督學習問題
如何優(yōu)化一個垃圾郵件分類器

誤差分析

推薦方案，如何快速實現一個可靠的機器學習系統(tǒng)。
手動分析
數字評估的重要性。

不均勻數據的評估方法

正樣本太少，即使全預測為負正確率也很高。
準確度和召回率詳解。

準確度和召回率的取舍

準確度和召回率的取舍
使用F1指標來評價。

設計一個高準確度學習系統(tǒng)

數據最重要。

作業(yè)習題

ex6.m

%% Machine Learning Online Class % Exercise 6 | Spam Classification with SVMs % % Instructions % ------------ % % This file contains code that helps you get started on the % exercise. You will need to complete the following functions: % % gaussianKernel.m % dataset3Params.m % processEmail.m % emailFeatures.m % % For this exercise, you will not need to change any code in this file, % or any other files other than those mentioned above. %%% Initialization clear ; close all; clc%% ==================== Part 1: Email Preprocessing ==================== % To use an SVM to classify emails into Spam v.s. Non-Spam, you first need % to convert each email into a vector of features. In this part, you will % implement the preprocessing steps for each email. You should % complete the code in processEmail.m to produce a word indices vector % for a given email.fprintf('\nPreprocessing sample email (emailSample1.txt)\n');% Extract Features file_contents = readFile('emailSample1.txt'); word_indices = processEmail(file_contents);% Print Stats fprintf('Word Indices: \n'); fprintf(' %d', word_indices); fprintf('\n\n');fprintf('Program paused. Press enter to continue.\n'); pause;%% ==================== Part 2: Feature Extraction ==================== % Now, you will convert each email into a vector of features in R^n. % You should complete the code in emailFeatures.m to produce a feature % vector for a given email.fprintf('\nExtracting features from sample email (emailSample1.txt)\n');% Extract Features file_contents = readFile('emailSample1.txt'); word_indices = processEmail(file_contents); features = emailFeatures(word_indices);% Print Stats fprintf('Length of feature vector: %d\n', length(features)); fprintf('Number of non-zero entries: %d\n', sum(features > 0));fprintf('Program paused. Press enter to continue.\n'); pause;%% =========== Part 3: Train Linear SVM for Spam Classification ======== % In this section, you will train a linear classifier to determine if an % email is Spam or Not-Spam.% Load the Spam Email dataset % You will have X, y in your environment load('spamTrain.mat');fprintf('\nTraining Linear SVM (Spam Classification)\n') fprintf('(this may take 1 to 2 minutes) ...\n')C = 0.1; model = svmTrain(X, y, C, @linearKernel);p = svmPredict(model, X);fprintf('Training Accuracy: %f\n', mean(double(p == y)) * 100);%% =================== Part 4: Test Spam Classification ================ % After training the classifier, we can evaluate it on a test set. We have % included a test set in spamTest.mat% Load the test dataset % You will have Xtest, ytest in your environment load('spamTest.mat');fprintf('\nEvaluating the trained Linear SVM on a test set ...\n')p = svmPredict(model, Xtest);fprintf('Test Accuracy: %f\n', mean(double(p == ytest)) * 100); pause;%% ================= Part 5: Top Predictors of Spam ==================== % Since the model we are training is a linear SVM, we can inspect the % weights learned by the model to understand better how it is determining % whether an email is spam or not. The following code finds the words with % the highest weights in the classifier. Informally, the classifier % 'thinks' that these words are the most likely indicators of spam. %% Sort the weights and obtin the vocabulary list [weight, idx] = sort(model.w, 'descend'); vocabList = getVocabList();fprintf('\nTop predictors of spam: \n'); for i = 1:15fprintf(' %-15s (%f) \n', vocabList{idx(i)}, weight(i)); endfprintf('\n\n'); fprintf('\nProgram paused. Press enter to continue.\n'); pause;%% =================== Part 6: Try Your Own Emails ===================== % Now that you've trained the spam classifier, you can use it on your own % emails! In the starter code, we have included spamSample1.txt, % spamSample2.txt, emailSample1.txt and emailSample2.txt as examples. % The following code reads in one of these emails and then uses your % learned SVM classifier to determine whether the email is Spam or % Not Spam% Set the file to be read in (change this to spamSample2.txt, % emailSample1.txt or emailSample2.txt to see different predictions on % different emails types). Try your own emails as well! filename = 'spamSample1.txt';% Read and predict file_contents = readFile(filename); word_indices = processEmail(file_contents); x = emailFeatures(word_indices); p = svmPredict(model, x);fprintf('\nProcessed %s\n\nSpam Classification: %d\n', filename, p); fprintf('(1 indicates spam, 0 indicates not spam)\n\n');

gaussiankernel.m

function sim = gaussianKernel(x1, x2, sigma) %RBFKERNEL returns a radial basis function kernel between x1 and x2 % sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 % and returns the value in sim% Ensure that x1 and x2 are column vectors x1 = x1(:); x2 = x2(:);% You need to return the following variables correctly. sim = 0;% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return the similarity between x1 % and x2 computed using a Gaussian kernel with bandwidth % sigma % % sim=exp(sum(-(x1-x2).^2/(2*sigma^2)));% =============================================================end

dataset3Params.m

function [C, sigma] = dataset3Params(X, y, Xval, yval) %DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise %where you select the optimal (C, sigma) learning parameters to use for SVM %with RBF kernel % [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and % sigma. You should complete this function to return the optimal C and % sigma based on a cross-validation set. %% You need to return the following variables correctly. C = 1; sigma = 0.3;% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return the optimal C and sigma % learning parameters found using the cross validation set. % You can use svmPredict to predict the labels on the cross % validation set. For example, % predictions = svmPredict(model, Xval); % will return the predictions on the cross validation set. % % Note: You can compute the prediction error using % mean(double(predictions ~= yval)) % C_vec=[0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]; sigma_vec=[0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]; len1=length(C_vec); len2=length(sigma_vec); er=zeros(len2,len1); for i=1:len2sigma=sigma_vec(i);for j=1:len1model= svmTrain(X, y, C_vec(j), @(x1, x2) gaussianKernel(x1, x2, sigma));predictions = svmPredict(model, Xval);er(i,j)=mean(double(predictions ~= yval));end end [min1,min2]=find(er==min(min(er))); sigma=sigma_vec(min1); C=C_vec(min2);% =========================================================================end

processemial.m

function word_indices = processEmail(email_contents) %PROCESSEMAIL preprocesses a the body of an email and %returns a list of word_indices % word_indices = PROCESSEMAIL(email_contents) preprocesses % the body of an email and returns a list of indices of the % words contained in the email. %% Load Vocabulary vocabList = getVocabList();% Init return value word_indices = [];% ========================== Preprocess Email ===========================% Find the Headers ( \n\n and remove ) % Uncomment the following lines if you are working with raw emails with the % full headers% hdrstart = strfind(email_contents, ([char(10) char(10)])); % email_contents = email_contents(hdrstart(1):end);% Lower case email_contents = lower(email_contents);% Strip all HTML % Looks for any expression that starts with < and ends with > and replace % and does not have any < or > in the tag it with a space email_contents = regexprep(email_contents, '<[^<>]+>', ' ');% Handle Numbers % Look for one or more characters between 0-9 email_contents = regexprep(email_contents, '[0-9]+', 'number');% Handle URLS % Look for strings starting with http:// or https:// email_contents = regexprep(email_contents, ...'(http|https)://[^\s]*', 'httpaddr');% Handle Email Addresses % Look for strings with @ in the middle email_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr');% Handle $ sign email_contents = regexprep(email_contents, '[$]+', 'dollar');% ========================== Tokenize Email ===========================% Output the email to screen as well fprintf('\n==== Processed Email ====\n\n');% Process file l = 0; lenl=length(vocabList); while ~isempty(email_contents)% Tokenize and also get rid of any punctuation[str, email_contents] = ...strtok(email_contents, ...[' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]);% Remove any non alphanumeric charactersstr = regexprep(str, '[^a-zA-Z0-9]', '');% Stem the word % (the porterStemmer sometimes has issues, so we use a try catch block)try str = porterStemmer(strtrim(str)); catch str == ' '; continue;end;% Skip the word if it is too shortif length(str) < 1continue;end% Look up the word in the dictionary and add to word_indices if% found% ====================== YOUR CODE HERE ======================% Instructions: Fill in this function to add the index of str to% word_indices if it is in the vocabulary. At this point% of the code, you have a stemmed word from the email in% the variable str. You should look up str in the% vocabulary list (vocabList). If a match exists, you% should add the index of the word to the word_indices% vector. Concretely, if str = 'action', then you should% look up the vocabulary list to find where in vocabList% 'action' appears. For example, if vocabList{18} =% 'action', then, you should add 18 to the word_indices % vector (e.g., word_indices = [word_indices ; 18]; ).% % Note: vocabList{idx} returns a the word with index idx in the% vocabulary list.% % Note: You can use strcmp(str1, str2) to compare two strings (str1 and% str2). It will return 1 only if the two strings are equivalent.%for i2=1:lenlif strcmp(str,vocabList{i2})word_indices=[word_indices;i2];endend% =============================================================% Print to screen, ensuring that the output lines are not too longif (l + length(str) + 1) > 78fprintf('\n');l = 0;endfprintf('%s ', str);l = l + length(str) + 1; end % Print footer fprintf('\n\n=========================\n'); end

emailFeatures.m

function x = emailFeatures(word_indices) %EMAILFEATURES takes in a word_indices vector and produces a feature vector %from the word indices % x = EMAILFEATURES(word_indices) takes in a word_indices vector and % produces a feature vector from the word indices. % Total number of words in the dictionary n = 1899;% You need to return the following variables correctly. x = zeros(n, 1);% ====================== YOUR CODE HERE ====================== % Instructions: Fill in this function to return a feature vector for the % given email (word_indices). To help make it easier to % process the emails, we have have already pre-processed each % email and converted each word in the email into an index in % a fixed dictionary (of 1899 words). The variable % word_indices contains the list of indices of the words % which occur in one email. % % Concretely, if an email has the text: % % The quick brown fox jumped over the lazy dog. % % Then, the word_indices vector for this text might look % like: % % 60 100 33 44 10 53 60 58 5 % % where, we have mapped each word onto a number, for example: % % the -- 60 % quick -- 100 % ... % % (note: the above numbers are just an example and are not the % actual mappings). % % Your task is take one such word_indices vector and construct % a binary feature vector that indicates whether a particular % word occurs in the email. That is, x(i) = 1 when word i % is present in the email. Concretely, if the word 'the' (say, % index 60) appears in the email, then x(60) = 1. The feature % vector should look like: % % x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]; % % j=length(word_indices); for i=1:jx(word_indices(i))=1; end % =========================================================================end

總結

以上是生活随笔為你收集整理的吴恩达 coursera ML 第十课总结+作业答案的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。