内容简介：这是我在德国海德堡大学于2018年6月28日所做的关于研讨会介绍了机器学习的基本知识。通过一个示例数据集，我在R中使用caret和h2o包完成了一个标准的机器学习工作流:所有的分析都是在R中使用RStudio进行的。有关包括R版本、操作系统和包版本在内的详细会话信息，请参阅本文末尾的sessionInfo()输出。
这是我在德国海德堡大学于2018年6月28日所做的关于 R的机器学习介绍 的研讨会的幻灯片。整个研讨会的代码可以在视频下面找到。
研讨会介绍了机器学习的基本知识。通过一个示例数据集，我在R中使用caret和h2o包完成了一个标准的机器学习工作流:
- 读取数据
- 探索性数据分析
- 缺失值
- 特征工程
- 训练和测试划分
- 使用随机森林，梯度提升，ANN等训练模型
- 超参数调优
设置
所有的分析都是在R中使用RStudio进行的。有关包括R版本、操作系统和包版本在内的详细会话信息，请参阅本文末尾的sessionInfo()输出。
所有的图片都是ggplot2生成。
- 包
library(tidyverse) library(readr) library(mice)
数据准备
这个分析例子我使用的数据集是 Breast Cancer Wisconsin (Diagnostic) Dataset 。这个数据集可以在 UC Irvine Machine Learning Repository 下载。
第一个数据集查看预测类：
- 恶性的
- 良性乳腺质量。
这些特征描述了细胞核的特性，是通过对乳腺肿块 细针吸出物(FNA) 的图像分析得出的:
- 样本ID(代码号)
- 丛厚度
- 细胞大小均匀性
- 细胞形状均匀性
- 边际附着力
- 单个上皮细胞大小
- 裸核数
- 乏味的染色质
- 正常核数
- 有丝分裂
- 类,即诊断
bc_data <- read_delim("datasets/breast-cancer-wisconsin.data.txt",
delim = ",",
col_names = c("sample_code_number",
"clump_thickness",
"uniformity_of_cell_size",
"uniformity_of_cell_shape",
"marginal_adhesion",
"single_epithelial_cell_size",
"bare_nuclei",
"bland_chromatin",
"normal_nucleoli",
"mitosis",
"classes")) %>%
mutate(bare_nuclei = as.numeric(bare_nuclei),
classes = ifelse(classes == "2", "benign",
ifelse(classes == "4", "malignant", NA)))
summary(bc_data)
数据缺失
md.pattern(bc_data, plot = FALSE)
bc_data <- bc_data %>% drop_na() %>% select(classes, everything(), -sample_code_number) head(bc_data)
缺失值可以用mice package来插补。
更多信息和教程与代码:
https://shirinsplayground.netlify.com/2018/04/flu_prediction/
数据探索
- 分类响应变量
ggplot(bc_data, aes(x = classes, fill = classes)) + geom_bar()
关于处理不平衡类更多信息： https://shiring.github.io/machine_learning/2017/04/02/unbalanced
- 回归响应变量
ggplot(bc_data, aes(x = clump_thickness)) + geom_histogram(bins = 10)
- 特征集
gather(bc_data, x, y, clump_thickness:mitosis) %>%
ggplot(aes(x = y, color = classes, fill = classes)) +
geom_density(alpha = 0.3) +
facet_wrap( ~ x, scales = "free", ncol = 3)
- 相关图
co_mat_benign <- filter(bc_data, classes == "benign") %>%
select(-1) %>%
cor()
co_mat_malignant <- filter(bc_data, classes == "malignant") %>%
select(-1) %>%
cor()
library(igraph)
g_benign <- graph.adjacency(co_mat_benign,
weighted = TRUE,
diag = FALSE,
mode = "upper")
g_malignant <- graph.adjacency(co_mat_malignant,
weighted = TRUE,
diag = FALSE,
mode = "upper")
# http://kateto.net/networks-r-igraph
cut.off_b <- mean(E(g_benign)$weight)
cut.off_m <- mean(E(g_malignant)$weight)
g_benign_2 <- delete_edges(g_benign, E(g_benign)[weight < cut.off_b])
g_malignant_2 <- delete_edges(g_malignant, E(g_malignant)[weight < cut.off_m])
c_g_benign_2 <- cluster_fast_greedy(g_benign_2)
c_g_malignant_2 <- cluster_fast_greedy(g_malignant_2)
par(mfrow = c(1,2))
plot(c_g_benign_2, g_benign_2,
vertex.size = colSums(co_mat_benign) * 10,
vertex.frame.color = NA,
vertex.label.color = "black",
vertex.label.cex = 0.8,
edge.width = E(g_benign_2)$weight * 15,
layout = layout_with_fr(g_benign_2),
main = "Benign tumors")
plot(c_g_malignant_2, g_malignant_2,
vertex.size = colSums(co_mat_malignant) * 10,
vertex.frame.color = NA,
vertex.label.color = "black",
vertex.label.cex = 0.8,
edge.width = E(g_malignant_2)$weight * 15,
layout = layout_with_fr(g_malignant_2),
main = "Malignant tumors")
主成分分析
library(ellipse)
# perform pca and extract scores
pcaOutput <- prcomp(as.matrix(bc_data[, -1]), scale = TRUE, center = TRUE)
pcaOutput2 <- as.data.frame(pcaOutput$x)
# define groups for plotting
pcaOutput2$groups <- bc_data$classes
centroids <- aggregate(cbind(PC1, PC2) ~ groups, pcaOutput2, mean)
conf.rgn <- do.call(rbind, lapply(unique(pcaOutput2$groups), function(t)
data.frame(groups = as.character(t),
ellipse(cov(pcaOutput2[pcaOutput2$groups == t, 1:2]),
centre = as.matrix(centroids[centroids$groups == t, 2:3]),
level = 0.95),
stringsAsFactors = FALSE)))
ggplot(data = pcaOutput2, aes(x = PC1, y = PC2, group = groups, color = groups)) +
geom_polygon(data = conf.rgn, aes(fill = groups), alpha = 0.2) +
geom_point(size = 2, alpha = 0.6) +
labs(color = "",
fill = "")
多维缩放
select(bc_data, -1) %>%
dist() %>%
cmdscale %>%
as.data.frame() %>%
mutate(group = bc_data$classes) %>%
ggplot(aes(x = V1, y = V2, color = group)) +
geom_point()
t-SNE降维
library(tsne)
select(bc_data, -1) %>%
dist() %>%
tsne() %>%
as.data.frame() %>%
mutate(group = bc_data$classes) %>%
ggplot(aes(x = V1, y = V2, color = group)) +
geom_point()
R的机器学习包
# configure multicore library(doParallel) cl <- makeCluster(detectCores()) registerDoParallel(cl) library(caret)
训练，验证和测试数据集
set.seed(42)
index <- createDataPartition(bc_data$classes, p = 0.7, list = FALSE)
train_data <- bc_data[index, ]
test_data <- bc_data[-index, ]
bind_rows(data.frame(group = "train", train_data),
data.frame(group = "test", test_data)) %>%
gather(x, y, clump_thickness:mitosis) %>%
ggplot(aes(x = y, color = group, fill = group)) +
geom_density(alpha = 0.3) +
facet_wrap( ~ x, scales = "free", ncol = 3)
回归
set.seed(42)
model_glm <- caret::train(clump_thickness ~ .,
data = train_data,
method = "glm",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
savePredictions = TRUE,
verboseIter = FALSE))
model_glm
predictions <- predict(model_glm, test_data)
# model_glm$finalModel$linear.predictors == model_glm$finalModel$fitted.values
data.frame(residuals = resid(model_glm),
predictors = model_glm$finalModel$linear.predictors) %>%
ggplot(aes(x = predictors, y = residuals)) +
geom_jitter() +
geom_smooth(method = "lm")
# y == train_data$clump_thickness
data.frame(residuals = resid(model_glm),
y = model_glm$finalModel$y) %>%
ggplot(aes(x = y, y = residuals)) +
geom_jitter() +
geom_smooth(method = "lm")
data.frame(actual = test_data$clump_thickness,
predicted = predictions) %>%
ggplot(aes(x = actual, y = predicted)) +
geom_jitter() +
geom_smooth(method = "lm")
分类
- 决策树
library(rpart)
library(rpart.plot)
set.seed(42)
fit <- rpart(classes ~ .,
data = train_data,
method = "class",
control = rpart.control(xval = 10,
minbucket = 2,
cp = 0),
parms = list(split = "information"))
rpart.plot(fit, extra = 100)
- 随机森林
随机森林 的预测是基于多分类树的生成。它们可以用于分类和回归任务。这里，我展示了一个分类任务。
set.seed(42)
model_rf <- caret::train(classes ~ .,
data = train_data,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 5,
repeats = 3,
savePredictions = TRUE,
verboseIter = FALSE))
当您指定saveforecasts = TRUE时，您可以使用model_rf$pred访问交叉验证结果。
model_rf
model_rf$finalModel$confusion
处理不平衡数据集
幸运的是，caret可以很容易地将过和欠抽样技术与交叉验证重采样结合在一起。我们可以简单地将抽样选项添加到我们的trainControl系统中，并选择欠抽样。剩下的和原来的模型一样。
set.seed(42)
model_rf_down <- caret::train(classes ~ .,
data = train_data,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
savePredictions = TRUE,
verboseIter = FALSE,
sampling = "down"))
model_rf_down
特征的重要性
imp <- model_rf$finalModel$importance imp[order(imp, decreasing = TRUE), ]
importance <- varImp(model_rf, scale = TRUE) plot(importance)
预测测试数据集
confusionMatrix(predict(model_rf, test_data), as.factor(test_data$classes))
results <- data.frame(actual = test_data$classes,
predict(model_rf, test_data, type = "prob"))
results$prediction <- ifelse(results$benign > 0.5, "benign",
ifelse(results$malignant > 0.5, "malignant", NA))
results$correct <- ifelse(results$actual == results$prediction, TRUE, FALSE)
ggplot(results, aes(x = prediction, fill = correct)) +
geom_bar(position = "dodge")
ggplot(results, aes(x = prediction, y = benign, color = correct, shape = correct)) + geom_jitter(size = 3, alpha = 0.6)
极梯度提升树
极端梯度提升(XGBoost) 是一种用于监督学习的 梯度提升 的快速改进实现。
XGBoost使用了一种更正则化的模型正规化来控制过度拟合，从而使其具有更好的性能。xgboost的开发人员陈天琦说
XGBoost是一个树集成模型，它表示一组分类树和回归树(CART)的预测总和。在这一点上，XGBoost类似于Random Forests，但它使用了一种不同的模型训练方法。可用于分类和回归任务。这里，我展示了一个分类任务。
set.seed(42)
model_xgb <- caret::train(classes ~ .,
data = train_data,
method = "xgbTree",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 5,
repeats = 3,
savePredictions = TRUE,
verboseIter = FALSE))
model_xgb
特征的重要性
importance <- varImp(model_xgb, scale = TRUE) plot(importance)
预测测试数据集
confusionMatrix(predict(model_xgb, test_data), as.factor(test_data$classes))
results <- data.frame(actual = test_data$classes,
predict(model_xgb, test_data, type = "prob"))
results$prediction <- ifelse(results$benign > 0.5, "benign",
ifelse(results$malignant > 0.5, "malignant", NA))
results$correct <- ifelse(results$actual == results$prediction, TRUE, FALSE)
ggplot(results, aes(x = prediction, fill = correct)) +
geom_bar(position = "dodge")
ggplot(results, aes(x = prediction, y = benign, color = correct, shape = correct)) + geom_jitter(size = 3, alpha = 0.6)
caret可用模型
https://topepo.github.io/caret/available-models.html
特征选择
对整个数据集进行特征选择会导致预测偏差，因此我们需要单独对训练数据进行整个建模过程!
- 相关性
所有特征之间的相关性通过corrplot包计算和可视化。然后，我将删除所有相关性大于0.7的特性，使该特征与较低的平均值保持一致。
library(corrplot) # calculate correlation matrix corMatMy <- cor(train_data[, -1]) corrplot(corMatMy, order = "hclust")
#Apply correlation filter at 0.70, highlyCor <- colnames(train_data[, -1])[findCorrelation(corMatMy, cutoff = 0.7, verbose = TRUE)]
highlyCor
train_data_cor <- train_data[, which(!colnames(train_data) %in% highlyCor)]
- 递归特征消除(RFE)
选择特征的另一种方法是使用递归特征消除。RFE使用一种随机森林算法来测试特征的组合，并对每个特征进行准确率评分。得分最高的组合通常是优选的。
set.seed(7)
results_rfe <- rfe(x = train_data[, -1],
y = as.factor(train_data$classes),
sizes = c(1:9),
rfeControl = rfeControl(functions = rfFuncs, method = "cv", number = 10))
predictors(results_rfe)
train_data_rfe <- train_data[, c(1, which(colnames(train_data) %in% predictors(results_rfe)))]
- 遗传算法（GA）
遗传算法(GA)是基于自然选择的进化原理开发出来的:它的目标是通过建模选择一组特定的基因型来优化个体群。在每一代(即迭代)中，每个个体的适应度都是根据他们的基因型来计算的。然后，优胜劣汰的个体被选出来生产下一代。这一代的个体将拥有由亲本等位基因重组而成的基因型。这些新的基因型将再次决定每个个体的适应性。这个选择过程在特定的世代中迭代，(理想地)导致在基因库中固定最适合的等位基因。
这种优化概念也可以应用于非进化模型，比如机器学习中的特征选择过程。
set.seed(27)
model_ga <- gafs(x = train_data[, -1],
y = as.factor(train_data$classes),
iters = 10, # generations of algorithm
popSize = 10, # population size for each generation
levels = c("malignant", "benign"),
gafsControl = gafsControl(functions = rfGA, # Assess fitness with RF
method = "cv", # 10 fold cross validation
genParallel = TRUE, # Use parallel programming
allowParallel = TRUE))
plot(model_ga)
train_data_ga <- train_data[, c(1, which(colnames(train_data) %in% model_ga$ga$final))]
用caret做超参数调优
- 笛卡尔网格
- mtry:在每一次分割中随机抽取的候选变量的数量。
set.seed(42)
grid <- expand.grid(mtry = c(1:10))
model_rf_tune_man <- caret::train(classes ~ .,
data = train_data,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
savePredictions = TRUE,
verboseIter = FALSE),
tuneGrid = grid)
model_rf_tune_man
plot(model_rf_tune_man)
- 随机选择
set.seed(42)
model_rf_tune_auto <- caret::train(classes ~ .,
data = train_data,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
savePredictions = TRUE,
verboseIter = FALSE,
search = "random"),
tuneGrid = grid,
tuneLength = 15)
model_rf_tune_auto
plot(model_rf_tune_auto)
使用h2o做网格搜索
R package h2o为 h2o 提供了一个方便的接口，h2o是一个开源的机器学习和深度学习平台。H2O为分类、回归和深度学习提供了广泛的通用机器学习算法。
library(h2o) h2o.init(nthreads = -1)
h2o.no_progress() bc_data_hf <- as.h2o(bc_data)
h2o.describe(bc_data_hf) %>%
gather(x, y, Zeros:Sigma) %>%
mutate(group = ifelse(x %in% c("Min", "Max", "Mean"), "min, mean, max",
ifelse(x %in% c("NegInf", "PosInf"), "Inf", "sigma, zeros"))) %>%
ggplot(aes(x = Label, y = as.numeric(y), color = x)) +
geom_point(size = 4, alpha = 0.6) +
scale_color_brewer(palette = "Set1") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
facet_grid(group ~ ., scales = "free") +
labs(x = "Feature",
y = "Value",
color = "")
library(reshape2) # for melting
bc_data_hf[, 1] <- h2o.asfactor(bc_data_hf[, 1])
cor <- h2o.cor(bc_data_hf)
rownames(cor) <- colnames(cor)
melt(cor) %>%
mutate(Var2 = rep(rownames(cor), nrow(cor))) %>%
mutate(Var2 = factor(Var2, levels = colnames(cor))) %>%
mutate(variable = factor(variable, levels = colnames(cor))) %>%
ggplot(aes(x = variable, y = Var2, fill = value)) +
geom_tile(width = 0.9, height = 0.9) +
scale_fill_gradient2(low = "white", high = "red", name = "Cor.") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "",
y = "")
训练，验证和测试数据集
splits <- h2o.splitFrame(bc_data_hf,
ratios = c(0.7, 0.15),
seed = 1)
train <- splits[[1]]
valid <- splits[[2]]
test <- splits[[3]]
response <- "classes"
features <- setdiff(colnames(train), response)
summary(as.factor(train$classes), exact_quantiles = TRUE) summary(as.factor(valid$classes), exact_quantiles = TRUE) summary(as.factor(test$classes), exact_quantiles = TRUE)
pca <- h2o.prcomp(training_frame = train,
x = features,
validation_frame = valid,
transform = "NORMALIZE",
impute_missing = TRUE,
k = 3,
seed = 42)
eigenvec <- as.data.frame(pca@model$eigenvectors)
eigenvec$label <- features
library(ggrepel)
ggplot(eigenvec, aes(x = pc1, y = pc2, label = label)) +
geom_point(color = "navy", alpha = 0.7) +
geom_text_repel()
分类
- 随机森林
hyper_params <- list(
ntrees = c(25, 50, 75, 100),
max_depth = c(10, 20, 30),
min_rows = c(1, 3, 5)
)
search_criteria <- list(
strategy = "RandomDiscrete",
max_models = 50,
max_runtime_secs = 360,
stopping_rounds = 5,
stopping_metric = "AUC",
stopping_tolerance = 0.0005,
seed = 42
)
f_grid <- h2o.grid(algorithm = "randomForest", # h2o.randomForest,
# alternatively h2o.gbm
# for Gradient boosting trees
x = features,
y = response,
grid_id = "rf_grid",
training_frame = train,
validation_frame = valid,
nfolds = 25,
fold_assignment = "Stratified",
hyper_params = hyper_params,
search_criteria = search_criteria,
seed = 42
)
# performance metrics where smaller is better -> order with decreasing = FALSE
sort_options_1 <- c("mean_per_class_error", "mse", "err", "logloss")
for (sort_by_1 in sort_options_1) {
grid <- h2o.getGrid("rf_grid", sort_by = sort_by_1, decreasing = FALSE)
model_ids <- grid@model_ids
best_model <- h2o.getModel(model_ids[[1]])
h2o.saveModel(best_model, path="models", force = TRUE)
}
# performance metrics where bigger is better -> order with decreasing = TRUE
sort_options_2 <- c("auc", "precision", "accuracy", "recall", "specificity")
for (sort_by_2 in sort_options_2) {
grid <- h2o.getGrid("rf_grid", sort_by = sort_by_2, decreasing = TRUE)
model_ids <- grid@model_ids
best_model <- h2o.getModel(model_ids[[1]])
h2o.saveModel(best_model, path = "models", force = TRUE)
}
files <- list.files(path = "models")
rf_models <- files[grep("rf_grid_model", files)]
for (model_id in rf_models) {
path <- paste0(getwd(), "/models/", model_id)
best_model <- h2o.loadModel(path)
mse_auc_test <- data.frame(model_id = model_id,
mse = h2o.mse(h2o.performance(best_model, test)),
auc = h2o.auc(h2o.performance(best_model, test)))
if (model_id == rf_models[[1]]) {
mse_auc_test_comb <- mse_auc_test
} else {
mse_auc_test_comb <- rbind(mse_auc_test_comb, mse_auc_test)
}
}
mse_auc_test_comb %>%
gather(x, y, mse:auc) %>%
ggplot(aes(x = model_id, y = y, fill = model_id)) +
facet_grid(x ~ ., scales = "free") +
geom_bar(stat = "identity", alpha = 0.8, position = "dodge") +
scale_fill_brewer(palette = "Set1") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
plot.margin = unit(c(0.5, 0, 0, 1.5), "cm")) +
labs(x = "", y = "value", fill = "")
for (model_id in rf_models) {
best_model <- h2o.getModel(model_id)
finalRf_predictions <- data.frame(model_id = rep(best_model@model_id,
nrow(test)),
actual = as.vector(test$classes),
as.data.frame(h2o.predict(object = best_model,
newdata = test)))
finalRf_predictions$accurate <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict,
"yes", "no")
finalRf_predictions$predict_stringent <- ifelse(finalRf_predictions$benign > 0.8,
"benign",
ifelse(finalRf_predictions$malignant
> 0.8, "malignant", "uncertain"))
finalRf_predictions$accurate_stringent <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict_stringent, "yes",
ifelse(finalRf_predictions$predict_stringent ==
"uncertain", "na", "no"))
if (model_id == rf_models[[1]]) {
finalRf_predictions_comb <- finalRf_predictions
} else {
finalRf_predictions_comb <- rbind(finalRf_predictions_comb, finalRf_predictions)
}
}
finalRf_predictions_comb %>%
ggplot(aes(x = actual, fill = accurate)) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "Set1") +
facet_wrap(~ model_id, ncol = 2) +
labs(fill = "Were\npredictions\naccurate?",
title = "Default predictions")
finalRf_predictions_comb %>%
subset(accurate_stringent != "na") %>%
ggplot(aes(x = actual, fill = accurate_stringent)) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "Set1") +
facet_wrap(~ model_id, ncol = 2) +
labs(fill = "Were\npredictions\naccurate?",
title = "Stringent predictions")
rf_model <- h2o.loadModel("models/rf_grid_model_0")
h2o.varimp_plot(rf_model)
h2o.mean_per_class_error(rf_model, train = TRUE, valid = TRUE, xval = TRUE)
h2o.confusionMatrix(rf_model, valid = TRUE)
plot(rf_model,
timestep = "number_of_trees",
metric = "classification_error")
plot(rf_model,
timestep = "number_of_trees",
metric = "logloss")
plot(rf_model,
timestep = "number_of_trees",
metric = "AUC")
plot(rf_model,
timestep = "number_of_trees",
metric = "rmse")
h2o.auc(rf_model, train = TRUE) h2o.auc(rf_model, valid = TRUE) h2o.auc(rf_model, xval = TRUE) perf <- h2o.performance(rf_model, test) perf
plot(perf)
perf@metrics$thresholds_and_metric_scores %>%
ggplot(aes(x = fpr, y = tpr)) +
geom_point() +
geom_line() +
geom_abline(slope = 1, intercept = 0) +
labs(x = "False Positive Rate",
y = "True Positive Rate")
h2o.logloss(perf) h2o.mse(perf) h2o.auc(perf) head(h2o.metric(perf))
finalRf_predictions <- data.frame(actual = as.vector(test$classes),
as.data.frame(h2o.predict(object = rf_model,
newdata = test)))
finalRf_predictions$accurate <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict, "yes", "no")
finalRf_predictions$predict_stringent <- ifelse(finalRf_predictions$benign > 0.8, "benign",
ifelse(finalRf_predictions$malignant
> 0.8, "malignant", "uncertain"))
finalRf_predictions$accurate_stringent <- ifelse(finalRf_predictions$actual ==
finalRf_predictions$predict_stringent, "yes",
ifelse(finalRf_predictions$predict_stringent ==
"uncertain", "na", "no"))
finalRf_predictions %>%
group_by(actual, predict) %>%
dplyr::summarise(n = n())
finalRf_predictions %>%
ggplot(aes(x = actual, fill = accurate)) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "Set1") +
labs(fill = "Were\npredictions\naccurate?",
title = "Default predictions")
finalRf_predictions %>%
subset(accurate_stringent != "na") %>%
ggplot(aes(x = actual, fill = accurate_stringent)) +
geom_bar(position = "dodge") +
scale_fill_brewer(palette = "Set1") +
labs(fill = "Were\npredictions\naccurate?",
title = "Stringent predictions")
df <- finalRf_predictions[, c(1, 3, 4)]
thresholds <- seq(from = 0, to = 1, by = 0.1)
prop_table <- data.frame(threshold = thresholds, prop_true_b = NA, prop_true_m = NA)
for (threshold in thresholds) {
pred <- ifelse(df$benign > threshold, "benign", "malignant")
pred_t <- ifelse(pred == df$actual, TRUE, FALSE)
group <- data.frame(df, "pred" = pred_t) %>%
group_by(actual, pred) %>%
dplyr::summarise(n = n())
group_b <- filter(group, actual == "benign")
prop_b <- sum(filter(group_b, pred == TRUE)$n) / sum(group_b$n)
prop_table[prop_table$threshold == threshold, "prop_true_b"] <- prop_b
group_m <- filter(group, actual == "malignant")
prop_m <- sum(filter(group_m, pred == TRUE)$n) / sum(group_m$n)
prop_table[prop_table$threshold == threshold, "prop_true_m"] <- prop_m
}
prop_table %>%
gather(x, y, prop_true_b:prop_true_m) %>%
ggplot(aes(x = threshold, y = y, color = x)) +
geom_point() +
geom_line() +
scale_color_brewer(palette = "Set1") +
labs(y = "proportion of true predictions",
color = "b: benign cases\nm: malignant cases")
如果你对更多的机器学习帖子感兴趣，可以在我的博客上查看一下machine_learning的分类列表 https://shirinsplayground.netlify.com/categories/#posts-list-machine-learning - https://shiring.github.io/categories.html#machine_learning-ref
stopCluster(cl) h2o.shutdown()
sessionInfo()
作者：Dr. Shirin Glander 原文链接： https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/
您有什么想法，请留言。
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