Hey there! Let's break this down clearly—first we'll cover the key dataset definitions, then dive into the differences between those two R code snippets.

These terms fall into two categories: core model evaluation splits, and specialized splits for advanced ensemble techniques like stacking.

Core Split Datasets (Train/Test/Validation) #

These are the standard splits used in almost all machine learning workflows:

• Training Data: The largest subset (usually 60-70% of total data) used to teach your model the relationship between features and your target variable. This is where the model learns patterns from raw data.
• Testing Data: A completely unseen subset (15-20%) reserved for final evaluation of your trained model. You only use this once (or very rarely) to avoid "overfitting to the test set"—it’s your measure of how well the model will perform on real-world data.
• Validation Data: A middle subset (15-20%) used during training to tune model hyperparameters (like tree depth in a random forest, or regularization strength in regression). It’s like a "practice test" for adjusting your model before the final evaluation on test data.

Ensembled & Blended Data (For Stacking/Advanced Ensembles) #

These are specialized subsets used specifically in stacking (a type of ensemble where you combine multiple models with a "meta-model"):

• Ensembled Data: This subset is used to train your base models (e.g., random forest, SVM, linear regression) in a stacking workflow. It’s essentially a portion of your training data dedicated to building the first layer of models.
• Blended Data: Also called a "holdout blending set", this subset is used to generate predictions from your trained base models. These predictions become new features, paired with the blended data’s true labels, to train the meta-model (the final layer that combines base model outputs). It’s similar to a validation set but serves a specific purpose in stacking, not just hyperparameter tuning.

Let’s break down what each snippet does and how they differ.

Snippet 1: Standard Train/Test/Validation Split for Ensemble Experiments #

set.seed(123)
ss <- sample(1:3, size=nrow(dataframe), replace=TRUE, prob=c(0.6,0.2,0.2))
train <- mtcars[ss==1,]
test <- mtcars[ss==2,]
cvr <- mtcars[ss==3,]

What it does: #

• This creates a probabilistic random split of the mtcars dataset (note: the code references dataframe but uses mtcars—likely a typo) into three subsets: 60% training, 20% test, 20% validation (cvr).
• set.seed(123) ensures the split is reproducible.
• replace=TRUE uses with-replacement sampling, which means a single row could appear in multiple subsets (this is unusual for standard dataset splits—probably a typo, should be replace=FALSE for a clean, non-overlapping split).

Purpose: #

This is a standard split for testing ensemble methods like bagging or boosting. You’d use train to build your ensemble models, cvr to tune their hyperparameters, and test to evaluate the final ensemble’s performance.

Snippet 2: Data Split for Stacking/Blending Workflows #

# shuffle and split the data into three parts
set.seed(1234)
finaltrain <- finaltrain[sample(nrow(finaltrain)),]  # Shuffle rows to randomize
split <- floor(nrow(finaltrain)/3)
ensembleData <- finaltrain[0:split,]
blenderData <- finaltrain[(split+1):(2*split),]
# (The missing part is likely the final holdout test set: finaltest <- finaltrain[(2*split+1):nrow(finaltrain),])

What it does: #

• First, it shuffles the finaltrain dataset to ensure randomness, then splits it into three roughly equal parts using row indices.
• Unlike the first snippet, this uses no-replacement splitting—every row goes to exactly one subset.

Key Differences from Snippet 1: #

1. Split Logic: Snippet 1 uses probabilistic weighting (60/20/20), while this snippet splits the data into three nearly equal chunks.
2. Specialized Purpose: This split is designed explicitly for stacking:
   • ensembleData: Trains your base models (the first layer of the stack).
   • blenderData: Generates predictions from base models, which become features for training the meta-model (the final layer that combines base outputs).
   • The unwritten third subset is the final test set, used to evaluate the full stacked model.
3. Sampling Method: No overlapping rows here—critical for stacking, since the blender data needs to be unseen by the base models to avoid overfitting.

内容的提问来源于stack exchange，提问作者Vivek Kulkarni