- Главная
- Разное
- Дизайн
- Бизнес и предпринимательство
- Аналитика
- Образование
- Развлечения
- Красота и здоровье
- Финансы
- Государство
- Путешествия
- Спорт
- Недвижимость
- Армия
- Графика
- Культурология
- Еда и кулинария
- Лингвистика
- Английский язык
- Астрономия
- Алгебра
- Биология
- География
- Детские презентации
- Информатика
- История
- Литература
- Маркетинг
- Математика
- Медицина
- Менеджмент
- Музыка
- МХК
- Немецкий язык
- ОБЖ
- Обществознание
- Окружающий мир
- Педагогика
- Русский язык
- Технология
- Физика
- Философия
- Химия
- Шаблоны, картинки для презентаций
- Экология
- Экономика
- Юриспруденция
Statistics toolbox презентация
Содержание
- 1. Statistics toolbox
- 2. Decision Tree functions
- 3. Функция ‘treefit’ - fit a tree-based model
- 4. Cluster analysis functions
- 5. Функция kmeans IDX = kmeans(X,k) [IDX,C] =
- 6. Параметр ‘distance’ 'sqEuclidean‘ - Squared Euclidean distance
- 7. Параметр ‘start’ Method used to choose the
- 8. Classification load fisheriris; gscatter(meas(:,1), meas(:,2), species,'rgb','osd'); xlabel('Sepal length'); ylabel('Sepal width');
- 9. Linear and quadratic discriminant analysis linclass =
- 10. Visualization regioning the plane [x,y] = meshgrid(4:.1:8,2:.1:4.5);
- 11. Decision trees tree = treefit(meas(:,1:2), species); [dtnum,dtnode,dtclass]
- 12. Iris classification tree
- 13. Тестирование качества классификации resubcost = treetest(tree,'resub'); [cost,secost,ntermnodes,bestlevel]
- 14. Выбор уровня [mincost,minloc] = min(cost); cutoff
- 15. Оптимальное дерево классификации prunedtree = treeprune(tree,bestlevel); treedisp(prunedtree) cost(bestlevel+1) >> ans = 0.22
- 16. Дендрограмма классификации ирисов eucD = pdist(meas,'euclidean'); clustTreeEuc
Decision Tree functions
Слайд 3Функция ‘treefit’ - fit a tree-based model for classification or regression.
Syntax: t = treefit(X,y)
Пример:
load fisheriris;
t = treefit(meas,species);
treedisp(t,'names',{'SL' 'SW' 'PL' 'PW'});
Слайд 5Функция kmeans
IDX = kmeans(X,k)
[IDX,C] = kmeans(X,k)
[IDX,C,sumd] = kmeans(X,k)
[IDX,C,sumd,D] = kmeans(X,k)
[...] =
kmeans(...,'param1',val1,'param2',val2,...)
IDX = kmeans(X, k) partitions the points in the n-by-p data matrix X into k clusters. This iterative partitioning minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. Rows of X correspond to points, columns correspond to variables. By default, kmeans uses squared Euclidean distances.
IDX - n-by-1 vector containing the cluster indices of each point.
C - k-by-p matrix cluster centroid locations.
sumd - 1-by-k vector within-cluster sums of point-to-centroid distances.
D - n-by-k matrix of distances from each point to every centroid.
IDX = kmeans(X, k) partitions the points in the n-by-p data matrix X into k clusters. This iterative partitioning minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. Rows of X correspond to points, columns correspond to variables. By default, kmeans uses squared Euclidean distances.
IDX - n-by-1 vector containing the cluster indices of each point.
C - k-by-p matrix cluster centroid locations.
sumd - 1-by-k vector within-cluster sums of point-to-centroid distances.
D - n-by-k matrix of distances from each point to every centroid.
Слайд 6Параметр ‘distance’
'sqEuclidean‘ - Squared Euclidean distance (default).
'cityblock‘ - Sum of
absolute differences, i.e., L1.
'cosine‘ - One minus the cosine of the included angle between points (treated as vectors).
'correlation‘ - One minus the sample correlation between points (treated as sequences of values).
'Hamming‘ - Percentage of bits that differ (only suitable for binary data).
'cosine‘ - One minus the cosine of the included angle between points (treated as vectors).
'correlation‘ - One minus the sample correlation between points (treated as sequences of values).
'Hamming‘ - Percentage of bits that differ (only suitable for binary data).
Слайд 7Параметр ‘start’
Method used to choose the initial cluster centroid positions, sometimes
known as "seeds". Valid starting values are:
'sample‘ - Select k observations from X at random (default).
'uniform‘ - Select k points uniformly at random from the range of X. Not valid with Hamming distance.
'cluster‘ - Perform a preliminary clustering phase on a random 10% subsample of X. This preliminary phase is itself initialized using 'sample'.
‘Matrix’ - k-by-p matrix of centroid starting locations. In this case, you can pass in [] for k, and kmeans infers k from the first dimension of the matrix. You can also supply a 3-dimensional array, implying a value for the 'replicates' parameter from the array's third dimension.
'sample‘ - Select k observations from X at random (default).
'uniform‘ - Select k points uniformly at random from the range of X. Not valid with Hamming distance.
'cluster‘ - Perform a preliminary clustering phase on a random 10% subsample of X. This preliminary phase is itself initialized using 'sample'.
‘Matrix’ - k-by-p matrix of centroid starting locations. In this case, you can pass in [] for k, and kmeans infers k from the first dimension of the matrix. You can also supply a 3-dimensional array, implying a value for the 'replicates' parameter from the array's third dimension.
Слайд 8Classification
load fisheriris;
gscatter(meas(:,1), meas(:,2), species,'rgb','osd');
xlabel('Sepal length');
ylabel('Sepal width');
Слайд 9Linear and quadratic discriminant analysis
linclass = classify(meas(:,1:2), meas(:,1:2),species);
bad = ~strcmp(linclass,species);
numobs =
size(meas,1);
pbad = sum(bad) / numobs;
hold on;
plot(meas(bad,1), meas(bad,2), 'kx');
hold off;
pbad = sum(bad) / numobs;
hold on;
plot(meas(bad,1), meas(bad,2), 'kx');
hold off;
Слайд 10Visualization regioning the plane
[x,y] = meshgrid(4:.1:8,2:.1:4.5);
x = x(:);
y = y(:);
j =
classify([x y],meas(:,1:2), species);
gscatter(x,y,j,'grb','sod')
gscatter(x,y,j,'grb','sod')
Слайд 11Decision trees
tree = treefit(meas(:,1:2), species);
[dtnum,dtnode,dtclass] = treeval(tree, meas(:,1:2));
bad = ~strcmp(dtclass,species);
sum(bad) /
numobs
Слайд 13Тестирование качества классификации
resubcost = treetest(tree,'resub');
[cost,secost,ntermnodes,bestlevel] = treetest(tree,'cross',meas(:,1:2),species);
plot(ntermnodes,cost,'b-', ntermnodes,resubcost,'r--')
figure(gcf);
xlabel('Number of terminal nodes');
ylabel('Cost
(misclassification error)')
legend('Cross-validation','Resubstitution')
legend('Cross-validation','Resubstitution')
Слайд 14Выбор уровня
[mincost,minloc] = min(cost);
cutoff = mincost + secost(minloc);
hold on
plot([0 20],
[cutoff cutoff], 'k:')
plot(ntermnodes(bestlevel+1), cost(bestlevel+1), 'mo')
legend('Cross-validation', 'Resubstitution', 'Min + 1 std. err.','Best choice')
hold off
plot(ntermnodes(bestlevel+1), cost(bestlevel+1), 'mo')
legend('Cross-validation', 'Resubstitution', 'Min + 1 std. err.','Best choice')
hold off
Слайд 15Оптимальное дерево классификации
prunedtree = treeprune(tree,bestlevel);
treedisp(prunedtree)
cost(bestlevel+1)
>> ans = 0.22
Слайд 16Дендрограмма классификации ирисов
eucD = pdist(meas,'euclidean');
clustTreeEuc = linkage(eucD,'average');
[h,nodes] = dendrogram(clustTreeEuc,0);
set(gca,'TickDir','out',
'TickLength',
[.002 0],'XTickLabel',[]);
