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ds/25-1/5/images/true_positives.jpg rename to 5/data science/5/images/true_positives.jpg diff --git a/ds/25-1/5/log_file.txt b/5/data science/5/log_file.txt similarity index 100% rename from ds/25-1/5/log_file.txt rename to 5/data science/5/log_file.txt diff --git a/5/data science/r/.RData b/5/data science/r/.RData new file mode 100644 index 0000000..ca8c439 Binary files /dev/null and b/5/data science/r/.RData differ diff --git a/5/data science/r/.Rhistory b/5/data science/r/.Rhistory new file mode 100644 index 0000000..6ac6ac7 --- /dev/null +++ b/5/data science/r/.Rhistory @@ -0,0 +1,226 @@ +a <- 3 +v <- c(1,2,3,4,5) +q() +a <- 3 +v<-c(1,2,3,4,5) +read.table("git/public/lab/ds/25-1/r2/zipIncome.txt") +db = read.table("git/public/lab/ds/25-1/r2/zipIncome.txt") +levels<-c(1,2,3) +ratings<-c("a", "b", "c") +f<-factor(ratings, levels) +print(f) +print(f.size()) +print(f) +print(levels.default()) +print(levels[0]) +print(levels[1]) +print(ratings[1]) +print(ratings[2]) +print(ratings[0]) +print(ratings[4]) +print(ratings[3]) +levels<-("a", "b", "a", "c") +levels<-("a", "b", "a", "c") +levels<-c("a", "b", "a", "c") +f<-factor(ratings, levels) +tmp<-levels +levels<-ratings +ratings<-tmp +remove(tmp) +f<-factor(ratings, levels) +f[0] +f[1] +f[2] +f[3] +f[4] +f[5] +pi +e +exp +exp() +v <- c(1:10) +w <- c(15:24) +x <- v +x = v +x <- v * w +db[1:3] +db[1:2] +db[1] +db[1,] +db[1:2,] +db[2,] +db[1,] +db[,1] +db$state +db +db$V1 +db$q +db[,1] +db[1,] +db$zip_prefixes +db$name +db["zip_prefixes"] +df <- data.frame(name=c("a")) +fd +df +df$name +df[] +df[1] +df[,1] +df[,2] +df[1,] +class(df) +printd +print(d) +summary(x) +var(x) +sd(x) +range(x) +plot(x) +hist(x) +str(x) +help(plot) +setwd('/home/sek1ro/git/public/lab/ds/25-1/r') +survey <- read.csv('survey.csv') +head(survey) +survey$price20 <- ifelse(survey$Price == 20, 1, 0) +survey$price30 <- ifelse(survey$Price == 30, 1, 0) +head(survey) +survey$one <- 1 +model <- glm( +MYDEPV ~ Income + Age + price20 + price30, +binomial(link = "logit"), +survey +) +summary(model) +quantile(residuals(model)) +pred_test = predict(tree, test_df, type="class") +setwd('/home/sek1ro/git/public/lab/ds/25-1/r') +survey <- read.csv('survey.csv') +train_df = survey[1:600,] +test_df = survey[601:750,] +setwd('/home/sek1ro/git/public/lab/ds/25-1/r') +survey <- read.csv('survey.csv') +train_df = survey[1:600,] +test_df = survey[601:750,] +library(rpart) +tree = rpart( +MYDEPV ~ Price + Income + Age, +data = train_df, +method = "class", +parms = list(split = "information"), +control = rpart.control( +xval = 3, +), +) +printcp(tree) +library(rpart.plot) +rpart.plot( +tree, +type = 1, +extra = 106, +#6 Class models: the probability of the second class only. Useful for binary responses. +#100 display the percentage of observations in the node. +fallen.leaves = TRUE, +) +pred_class = predict(tree, train_df, type="class") +conf_mat = table( +Actual = train_df$MYDEPV, +Predicted = pred_class +) +conf_mat +print(diag(conf_mat) / rowSums(conf_mat)) +print(1 - sum(diag(conf_mat)) / sum(conf_mat)) +pred_prob = predict(tree, train_df, type="prob")[,2] +library(ROCR) +pred = prediction(pred_prob, train_df$MYDEPV) +perf = performance(pred, "tpr", "fpr") +plot(perf) +abline(a = 0, b = 1) +auc_perf = performance(pred, measure = "auc") +auc_perf@y.values[[1]] +pred_test = predict(tree, test_df, type="class") +conf_mat_test = table(Actual = test_df$MYDEPV, Predicted = pred_test) +conf_mat_test +print(diag(conf_mat) / rowSums(conf_mat)) +tree_gini = rpart( +MYDEPV ~ Price + Income + Age, +data = train_df, +method = "class", +parms = list(split = "gini") +) +printcp(tree_gini) +rpart.plot( +tree_gini, +type = 1, +extra = 106, +fallen.leaves = TRUE, +) +best_cp <- tree$cptable[which.min(tree_dini$cptable[, "xerror"]), "CP"] +best_cp <- tree_dini$cptable[which.min(tree_dini$cptable[, "xerror"]), "CP"] +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp +pruned_tree = prune(tree_gini, cp = best_cp) +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp +pruned_tree = prune(tree_gini, cp = best_cp) +printcp(pruned_tree) +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp +pruned_tree = prune(tree_gini, cp = best_cp) +printcp(pruned_tree) +rpart.plot(pruned_tree) +pred_test = predict(tree, test_df, type="class") +conf_mat_test = table(Actual = test_df$MYDEPV, Predicted = pred_test) +conf_mat_test +print(diag(conf_mat_test) / rowSums(conf_mat_test)) +tree_gini = rpart( +MYDEPV ~ Price + Income + Age, +data = train_df, +method = "class", +parms = list(split = "gini") +) +printcp(tree_gini) +rpart.plot( +tree_gini, +type = 1, +extra = 106, +fallen.leaves = TRUE, +) +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp +pruned_tree = prune(tree_gini, cp = best_cp) +printcp(pruned_tree) +rpart.plot(pruned_tree) +pruned_pred = predict(pruned_tree, test_df, type="class") +pruned_conf_mat = table(Actual = test_df$MYDEPV, Predicted = pruned_pred) +pruned_conf_mat +print(diag(pruned_conf_mat) / rowSums(pruned_conf_mat)) +pred_test = predict(tree, test_df, type="class") +conf_mat_test = table(Actual = test_df$MYDEPV, Predicted = pred_test) +conf_mat_test +print(diag(conf_mat_test) / rowSums(conf_mat_test)) +tree_gini = rpart( +MYDEPV ~ Price + Income + Age, +data = train_df, +method = "class", +parms = list(split = "gini") +) +printcp(tree_gini) +rpart.plot( +tree_gini, +type = 1, +extra = 106, +fallen.leaves = TRUE, +) +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] 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science/r/.Rproj.user/C6239C96/sources/per/t/36F8AE4B-contents b/5/data science/r/.Rproj.user/C6239C96/sources/per/t/36F8AE4B-contents new file mode 100644 index 0000000..f679659 --- /dev/null +++ b/5/data science/r/.Rproj.user/C6239C96/sources/per/t/36F8AE4B-contents @@ -0,0 +1,161 @@ +--- +title: "Lab9: Decision trees" +author: "Vladislav Litvinov " +output: + pdf_document: + toc_float: TRUE +--- +# Data preparation +```{r} +setwd('/home/sek1ro/git/public/lab/ds/25-1/r') +survey <- read.csv('survey.csv') + +train_df = survey[1:600,] +test_df = survey[601:750,] +``` +# Building classification tree +decision formula is MYDEPV ~ Price + Income + Age + +Use three-fold cross-validation and the information gain splitting index +Which features were actually used to construct the tree? +Plot the tree using the “rpart.plot” package. + +Three-fold cross-validation - Делают 3 прогона: +Прогон 1: обучаемся на B + C, тестируем на A +Прогон 2: обучаемся на A + C, тестируем на B +Прогон 3: обучаемся на A + B, тестируем на C + +Получаем 3 значения метрики (accuracy, F1, MSE и т.п.). +Берём среднее значение — это и есть итоговая оценка качества модели. + +rpart сам отбрасывает признаки, если они не улучшают разбиение по information gain. + +CP-table - связь сложности дерева и ошибки +Root node error — ошибка без разбиений +nsplit — число split-ов +rel error — обучающая ошибка относительно корня +xerror — ошибка по cross-validation +xstd — стандартное отклонение xerror + +type — расположение split-ов +extra — доп. информация в узлах +fallen.leaves — выравнивание листьев + +H = -x\cdot\log\left(x\right)-\left(1-x\right)\log\left(1-x\right) +Gain(A) = Info(S) - Info(S_A) - максимизируем + +Ранняя остановка. Ограничение грубины. Минимальное количество примеров в узле. + +Отсечение ветвей. +Строительство полного дерева, в котором листья содержат примеры одного класса. +Определение двух показателей: относительную точность модели и абсолютную ошибку. +Удаление листов и узлов, потеря которых минимально скажется на точности модели и увеличении ошибки. + + +```{r} +library(rpart) +tree = rpart( + MYDEPV ~ Price + Income + Age, + data = train_df, + method = "class", + parms = list(split = "information"), + control = rpart.control( + xval = 3, + ), +) +printcp(tree) + +library(rpart.plot) + +rpart.plot( + tree, + type = 1, + extra = 106, + #6 Class models: the probability of the second class only. Useful for binary responses. + #100 display the percentage of observations in the node. + fallen.leaves = TRUE, +) +``` +Score the model with the training data and create the model’s confusion matrix. Which class of MYDEPV was the model better able to classify? +```{r} +pred_class = predict(tree, train_df, type="class") + +conf_mat = table( + Actual = train_df$MYDEPV, + Predicted = pred_class +) + +conf_mat +print(diag(conf_mat) / rowSums(conf_mat)) +``` +Define the resubstitution error rate, and then calculate it using the confusion matrix from the previous step. Is it a good indicator of predictive performance? Why or why not? + +Resubstitution error rate — это доля неправильных предсказаний на тех же данных, на которых обучалась модель +```{r} +print(1 - sum(diag(conf_mat)) / sum(conf_mat)) +``` +ROC curve - Receiver Operating Characteristic +x - FPR = FP / (FP + TN) +y - TPR = TP / (TP + FN) +```{r} +pred_prob = predict(tree, train_df, type="prob")[,2] + +library(ROCR) +pred = prediction(pred_prob, train_df$MYDEPV) +perf = performance(pred, "tpr", "fpr") + +plot(perf) +abline(a = 0, b = 1) + +auc_perf = performance(pred, measure = "auc") +auc_perf@y.values[[1]] +``` +Score the model with the testing data. How accurate are the tree’s predictions? +Repeat part (a), but set the splitting index to the Gini coefficient splitting index. How does the new tree compare to the previous one? + +индекс Джини показывает, как часто случайно выбранный пример обучающего множества будет распознан неправильно. + +Gini(Q) = 1 - sum(p^2) - максимизируем +0 - все к 1 классу +1 - все равновероятны +1-\ x^{2}\ -\ \left(1-x\right)^{2} +```{r} +pred_test = predict(tree, test_df, type="class") +conf_mat_test = table(Actual = test_df$MYDEPV, Predicted = pred_test) +conf_mat_test +print(diag(conf_mat_test) / rowSums(conf_mat_test)) + +tree_gini = rpart( + MYDEPV ~ Price + Income + Age, + data = train_df, + method = "class", + parms = list(split = "gini") +) + +printcp(tree_gini) + +rpart.plot( + tree_gini, + type = 1, + extra = 106, + fallen.leaves = TRUE, +) +``` +One way to prune a tree is according to the complexity parameter associated with the smallest cross-validation error. Prune the new tree in this way using the “prune” function. Which features were actually used in the pruned tree? Why were certain variables not used? +```{r} +best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, "xerror"]), "CP"] +best_cp + +pruned_tree = prune(tree_gini, cp = best_cp) + +printcp(pruned_tree) + +rpart.plot(pruned_tree) +``` +Create the confusion matrix for the new model, and compare the performance of the model before and after pruning. +```{r} +pruned_pred = predict(pruned_tree, test_df, type="class") +pruned_conf_mat = table(Actual = test_df$MYDEPV, Predicted = pruned_pred) +pruned_conf_mat +print(diag(pruned_conf_mat) / rowSums(pruned_conf_mat)) +``` \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/C6239C96/sources/prop/231EDFBF b/5/data science/r/.Rproj.user/C6239C96/sources/prop/231EDFBF new file mode 100644 index 0000000..e4feea4 --- /dev/null +++ b/5/data science/r/.Rproj.user/C6239C96/sources/prop/231EDFBF @@ -0,0 +1,6 @@ +{ + "source_window_id": "", + "Source": "Source", + "cursorPosition": "153,3", + "scrollLine": "154" +} \ No newline at end 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+{"chunk_definitions":[{"row":14,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-3","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cw3y8fjmo2ayt","chunk_label":"unnamed-chunk-1"},{"row":77,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-4","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cgb1v2g83kknt","chunk_label":"unnamed-chunk-2"},{"row":89,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-5","dev":"png"},"document_id":"36F8AE4B","chunk_id":"c3jleyvkqxnqm","chunk_label":"unnamed-chunk-3"},{"row":95,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-6","dev":"png"},"document_id":"36F8AE4B","chunk_id":"c60fx7tj15bk5","chunk_label":"unnamed-chunk-4"},{"row":111,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-7","dev":"png"},"document_id":"36F8AE4B","chunk_id":"csdwusaa8puvd","chunk_label":"unnamed-chunk-5"},{"row":142,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-18","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cr3h7jd3nr0ya","chunk_label":"unnamed-chunk-6"},{"row":153,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-19","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cpyo5ihaht7o1","chunk_label":"unnamed-chunk-7"},{"row":160,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-20","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cce5y7xzr9zk6","chunk_label":"unnamed-chunk-8"}],"doc_write_time":1769443515} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c3jleyvkqxnqm/00000f.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c3jleyvkqxnqm/00000f.csv new file mode 100644 index 0000000..76be91a --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c3jleyvkqxnqm/00000f.csv @@ -0,0 +1,32 @@ +"0","pred_class = predict(tree, train_df, type=""class"")" +"0","" +"0","conf_mat = table(" +"0"," Actual = train_df$MYDEPV," +"0"," Predicted = pred_class" +"0",")" +"0","" +"0","conf_mat" +"1"," Predicted +" +"1","Actual" +"1"," 0" +"1"," 1" +"1"," + 0" +"1"," 314" +"1"," 26" +"1"," + 1" +"1"," 19" +"1"," 241" +"1"," +" +"0","print(diag(conf_mat) / rowSums(conf_mat))" +"1"," 0 " +"1"," 1 " +"1"," +" +"1","0.9235294 " +"1","0.9269231 " +"1"," +" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c60fx7tj15bk5/00000f.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c60fx7tj15bk5/00000f.csv new file mode 100644 index 0000000..620fcf3 --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/c60fx7tj15bk5/00000f.csv @@ -0,0 +1,5 @@ +"0","print(1 - sum(diag(conf_mat)) / sum(conf_mat))" +"1","[1]" +"1"," 0.075" +"1"," +" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cce5y7xzr9zk6/00000f.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cce5y7xzr9zk6/00000f.csv new file mode 100644 index 0000000..be1630e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cce5y7xzr9zk6/00000f.csv @@ -0,0 +1,27 @@ +"0","pruned_pred = predict(pruned_tree, test_df, type=""class"")" +"0","pruned_conf_mat = table(Actual = test_df$MYDEPV, Predicted = pruned_pred)" +"0","pruned_conf_mat" +"1"," Predicted +" +"1","Actual" +"1"," 0" +"1"," 1" +"1"," + 0" +"1"," 82" +"1"," 4" +"1"," + 1" +"1"," 13" +"1"," 51" +"1"," +" +"0","print(diag(pruned_conf_mat) / rowSums(pruned_conf_mat))" +"1"," 0 " +"1"," 1 " +"1"," +" +"1","0.9534884 " +"1","0.7968750 " +"1"," +" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/00000f.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/00000f.csv new file mode 100644 index 0000000..fad3baa --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/00000f.csv @@ -0,0 +1,107 @@ +"0","library(rpart)" +"0","tree = rpart(" +"0"," MYDEPV ~ Price + Income + Age," +"0"," data = train_df," +"0"," method = ""class""," +"0"," parms = list(split = ""information"")," +"0"," control = rpart.control(" +"0"," xval = 3," +"0"," )," +"0",")" +"0","printcp(tree)" +"1"," +Classification tree: +" +"1","rpart(formula = MYDEPV ~ Price + Income + Age, data = train_df, +" +"1"," method = ""class"", parms = list(split = ""information""), control = rpart.control(xval = 3, +" +"1"," )) +" +"1"," +" +"1","Variables actually used in tree construction: +" +"1","[1]" +"1"," Age " +"1"," Income" +"1"," Price " +"1"," +" +"1"," +" +"1","Root node error: " +"1","" +"1","260" +"1","" +"1","/" +"1","" +"1","600" +"1","" +"1"," = " +"1","" +"1","0.43333" +"1","" +"1"," + +" +"1","n=" +"1"," " +"1","600" +"1"," " +"1"," + +" +"1"," " +"1"," CP" +"1"," nsplit" +"1"," rel error" +"1"," xerror" +"1"," xstd" +"1"," +1" +"1"," 0.692308" +"1"," 0" +"1"," 1.00000" +"1"," 1.00000" +"1"," 0.046685" +"1"," +2" +"1"," 0.025000" +"1"," 1" +"1"," 0.30769" +"1"," 0.31154" +"1"," 0.032194" +"1"," +3" +"1"," 0.011538" +"1"," 3" +"1"," 0.25769" +"1"," 0.27308" +"1"," 0.030430" +"1"," +4" +"1"," 0.010256" +"1"," 5" +"1"," 0.23462" +"1"," 0.26923" +"1"," 0.030244" +"1"," +5" +"1"," 0.010000" +"1"," 11" +"1"," 0.17308" +"1"," 0.26923" +"1"," 0.030244" +"1"," +" +"0","library(rpart.plot)" +"0","" +"0","rpart.plot(" +"0"," tree," +"0"," type = 1," +"0"," extra = 106," +"0"," #6 Class models: the probability of the second class only. Useful for binary responses." +"0"," #100 display the percentage of observations in the node. " +"0"," fallen.leaves = TRUE," +"0",")" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.metadata b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.metadata new file mode 100644 index 0000000..dab240e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.metadata @@ -0,0 +1 @@ +{"height":432.6328800988875,"width":700.0,"dpi":-1.0,"size_behavior":0,"conditions":[]} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.png b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.png new file mode 100644 index 0000000..d45afcb Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.png differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.snapshot b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.snapshot new file mode 100644 index 0000000..4452d72 Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cgb1v2g83kknt/000010.snapshot differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/chunks.json b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/chunks.json new file mode 100644 index 0000000..ccf5e28 --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/chunks.json @@ -0,0 +1 @@ +{"chunk_definitions":[{"row":14,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-3","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cw3y8fjmo2ayt","chunk_label":"unnamed-chunk-1"},{"row":77,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-4","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cgb1v2g83kknt","chunk_label":"unnamed-chunk-2"},{"row":89,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-5","dev":"png"},"document_id":"36F8AE4B","chunk_id":"c3jleyvkqxnqm","chunk_label":"unnamed-chunk-3"},{"row":95,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-6","dev":"png"},"document_id":"36F8AE4B","chunk_id":"c60fx7tj15bk5","chunk_label":"unnamed-chunk-4"},{"row":111,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-7","dev":"png"},"document_id":"36F8AE4B","chunk_id":"csdwusaa8puvd","chunk_label":"unnamed-chunk-5"},{"row":142,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-18","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cr3h7jd3nr0ya","chunk_label":"unnamed-chunk-6"},{"row":153,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-19","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cpyo5ihaht7o1","chunk_label":"unnamed-chunk-7"},{"row":160,"row_count":1,"visible":true,"expansion_state":0,"options":{"engine":"r","label":"unnamed-chunk-20","dev":"png"},"document_id":"36F8AE4B","chunk_id":"cce5y7xzr9zk6","chunk_label":"unnamed-chunk-8"}],"doc_write_time":1769443515} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000011.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000011.csv new file mode 100644 index 0000000..e494416 --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000011.csv @@ -0,0 +1,79 @@ +"0","best_cp <- tree_gini$cptable[which.min(tree_gini$cptable[, ""xerror""]), ""CP""]" +"0","best_cp" +"1","[1]" +"1"," 0.01153846" +"1"," +" +"0","pruned_tree = prune(tree_gini, cp = best_cp)" +"0","" +"0","printcp(pruned_tree)" +"1"," +Classification tree: +" +"1","rpart(formula = MYDEPV ~ Price + Income + Age, data = train_df, +" +"1"," method = ""class"", parms = list(split = ""gini"")) +" +"1"," +" +"1","Variables actually used in tree construction: +" +"1","[1]" +"1"," Income" +"1"," Price " +"1"," +" +"1"," +" +"1","Root node error: " +"1","" +"1","260" +"1","" +"1","/" +"1","" +"1","600" +"1","" +"1"," = " +"1","" +"1","0.43333" +"1","" +"1"," + +" +"1","n=" +"1"," " +"1","600" +"1"," " +"1"," + +" +"1"," " +"1"," CP" +"1"," nsplit" +"1"," rel error" +"1"," xerror" +"1"," xstd" +"1"," +1" +"1"," 0.692308" +"1"," 0" +"1"," 1.00000" +"1"," 1.00000" +"1"," 0.046685" +"1"," +2" +"1"," 0.025000" +"1"," 1" +"1"," 0.30769" +"1"," 0.31154" +"1"," 0.032194" +"1"," +3" +"1"," 0.011538" +"1"," 3" +"1"," 0.25769" +"1"," 0.26538" +"1"," 0.030055" +"1"," +" +"0","rpart.plot(pruned_tree)" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.metadata b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.metadata new file mode 100644 index 0000000..dab240e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.metadata @@ -0,0 +1 @@ +{"height":432.6328800988875,"width":700.0,"dpi":-1.0,"size_behavior":0,"conditions":[]} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.png b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.png new file mode 100644 index 0000000..1b8cc82 Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.png differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.snapshot b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.snapshot new file mode 100644 index 0000000..9282549 Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cpyo5ihaht7o1/000012.snapshot differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000015.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000015.csv new file mode 100644 index 0000000..c492c2b --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000015.csv @@ -0,0 +1,125 @@ +"0","pred_test = predict(tree, test_df, type=""class"")" +"0","conf_mat_test = table(Actual = test_df$MYDEPV, Predicted = pred_test)" +"0","conf_mat_test" +"1"," Predicted +" +"1","Actual" +"1"," 0" +"1"," 1" +"1"," + 0" +"1"," 76" +"1"," 10" +"1"," + 1" +"1"," 6" +"1"," 58" +"1"," +" +"0","print(diag(conf_mat_test) / rowSums(conf_mat_test))" +"1"," 0 " +"1"," 1 " +"1"," +" +"1","0.8837209 " +"1","0.9062500 " +"1"," +" +"0","tree_gini = rpart(" +"0"," MYDEPV ~ Price + Income + Age," +"0"," data = train_df," +"0"," method = ""class""," +"0"," parms = list(split = ""gini"")" +"0",")" +"0","" +"0","printcp(tree_gini)" +"1"," +Classification tree: +" +"1","rpart(formula = MYDEPV ~ Price + Income + Age, data = train_df, +" +"1"," method = ""class"", parms = list(split = ""gini"")) +" +"1"," +" +"1","Variables actually used in tree construction: +" +"1","[1]" +"1"," Age " +"1"," Income" +"1"," Price " +"1"," +" +"1"," +" +"1","Root node error: " +"1","" +"1","260" +"1","" +"1","/" +"1","" +"1","600" +"1","" +"1"," = " +"1","" +"1","0.43333" +"1","" +"1"," + +" +"1","n=" +"1"," " +"1","600" +"1"," " +"1"," + +" +"1"," " +"1"," CP" +"1"," nsplit" +"1"," rel error" +"1"," xerror" +"1"," xstd" +"1"," +1" +"1"," 0.692308" +"1"," 0" +"1"," 1.00000" +"1"," 1.00000" +"1"," 0.046685" +"1"," +2" +"1"," 0.025000" +"1"," 1" +"1"," 0.30769" +"1"," 0.31154" +"1"," 0.032194" +"1"," +3" +"1"," 0.011538" +"1"," 3" +"1"," 0.25769" +"1"," 0.26538" +"1"," 0.030055" +"1"," +4" +"1"," 0.010256" +"1"," 5" +"1"," 0.23462" +"1"," 0.28846" +"1"," 0.031157" +"1"," +5" +"1"," 0.010000" +"1"," 11" +"1"," 0.17308" +"1"," 0.28462" +"1"," 0.030978" +"1"," +" +"0","rpart.plot(" +"0"," tree_gini," +"0"," type = 1," +"0"," extra = 106," +"0"," fallen.leaves = TRUE," +"0",")" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.metadata b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.metadata new file mode 100644 index 0000000..dab240e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.metadata @@ -0,0 +1 @@ +{"height":432.6328800988875,"width":700.0,"dpi":-1.0,"size_behavior":0,"conditions":[]} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.png b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.png new file mode 100644 index 0000000..d45afcb Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.png differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.snapshot b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.snapshot new file mode 100644 index 0000000..729a63c Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cr3h7jd3nr0ya/000016.snapshot differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/00000f.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/00000f.csv new file mode 100644 index 0000000..e24d57e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/00000f.csv @@ -0,0 +1,7 @@ +"0","pred_prob = predict(tree, train_df, type=""prob"")[,2]" +"0","" +"0","library(ROCR)" +"0","pred = prediction(pred_prob, train_df$MYDEPV)" +"0","perf = performance(pred, ""tpr"", ""fpr"")" +"0","" +"0","plot(perf)" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000011.csv b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000011.csv new file mode 100644 index 0000000..ff4f958 --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000011.csv @@ -0,0 +1 @@ +"0","abline(a = 0, b = 1)" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.metadata b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.metadata new file mode 100644 index 0000000..dab240e --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.metadata @@ -0,0 +1 @@ +{"height":432.6328800988875,"width":700.0,"dpi":-1.0,"size_behavior":0,"conditions":[]} \ No newline at end of file diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.png b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.png new file mode 100644 index 0000000..0dc7455 Binary files /dev/null and b/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.png differ diff --git a/5/data science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/csdwusaa8puvd/000012.snapshot b/5/data 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science/r/.Rproj.user/shared/notebooks/EB7B11F9-9/1/s/cw3y8fjmo2ayt/00000f.csv @@ -0,0 +1,5 @@ +"0","setwd('/home/sek1ro/git/public/lab/ds/25-1/r')" +"0","survey <- read.csv('survey.csv')" +"0","" +"0","train_df = survey[1:600,]" +"0","test_df = survey[601:750,]" diff --git a/5/data science/r/.Rproj.user/shared/notebooks/patch-chunk-names b/5/data science/r/.Rproj.user/shared/notebooks/patch-chunk-names new file mode 100644 index 0000000..e69de29 diff --git a/5/data science/r/.Rproj.user/shared/notebooks/paths b/5/data science/r/.Rproj.user/shared/notebooks/paths new file mode 100644 index 0000000..698296b --- /dev/null +++ b/5/data science/r/.Rproj.user/shared/notebooks/paths @@ -0,0 +1 @@ +/home/sek1ro/git/public/lab/ds/25-1/r/9.Rmd="EB7B11F9" diff --git a/ds/25-1/r/10.Rmd b/5/data science/r/10.Rmd similarity index 97% rename from ds/25-1/r/10.Rmd rename to 5/data science/r/10.Rmd index 30828f4..8907c2b 100644 --- a/ds/25-1/r/10.Rmd +++ b/5/data science/r/10.Rmd @@ -36,7 +36,7 @@ ACF(k) = Corr(x[t], x[t-k]) - Autocorrelation Function, показывает, н PACF - Partial Autocorrelation Function - оказывает чистую связь после удаления влияния всех промежуточных значений между t и t-k, это последний коэффициент в AR(k)-регрессии -xt = f1xt-1 + f2xt-2 + .. + fkxt-k + eps +xt = f1xt-1 + f2xt-2 + .. + fkxt-k + eps + f1xt-1 + f2xt-2 + .. + fkxt-k PACF(k) = fk ARMA(p, q) diff --git a/ds/25-1/r/12.1/ex4.pdf b/5/data science/r/12.1/ex4.pdf similarity index 100% rename from ds/25-1/r/12.1/ex4.pdf rename to 5/data science/r/12.1/ex4.pdf diff --git a/ds/25-1/r/12.1/mlclass-ex4/checkNNGradients.m b/5/data science/r/12.1/mlclass-ex4/checkNNGradients.m similarity index 100% rename from ds/25-1/r/12.1/mlclass-ex4/checkNNGradients.m rename to 5/data science/r/12.1/mlclass-ex4/checkNNGradients.m diff --git a/ds/25-1/r/12.1/mlclass-ex4/computeNumericalGradient.m b/5/data science/r/12.1/mlclass-ex4/computeNumericalGradient.m similarity index 100% rename from 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b/README.md deleted file mode 100644 index 855b430..0000000 --- a/README.md +++ /dev/null @@ -1,9 +0,0 @@ -### Структура директорий: - -> ./ предмет / год-семестр / номер_лабораторной /* - -год-семестр: -`24-1` - осенний семетр 24-года -`24-2` - весенний семестр 24-года - -номер_лабораторной: по порядку выполнения \ No newline at end of file