Training with machine_learning

[supermarco]

Hello,

What is the best approach for training a model on panel data ? Which package on R?
In detail: should i use a time series data sampling year year of the panel as if it was time series or does it exist a good approach handling panel data in terms of sampling?

Thanks a lot.

[supermarco]

Hello

I try to use the object recipe() and workflow() to fit machine_learning models. I would like to compare their performance with other function that i create. Do you know if we can use any function (say x² + 2 x -3) with these objects in place of a machine_learning model (say neural_network)?

Thank you

[IeeeXpert]

Training a model on panel data requires a thoughtful approach since panel datasets combine cross-sectional and time-series dimensions—typically data on multiple entities (like individuals, firms, or countries) observed over time.

Best Approach for Panel Data Modeling

1. Understand the Structure:

• Panel data = Cross-sectional units (e.g., students, firms) × Time dimension (e.g., years).
• Unlike pure time-series, panel data allows you to control for heterogeneity across entities.

2. Recommended Techniques:

• Use Fixed Effects or Random Effects models depending on the assumption about entity-specific variations.
• Apply Pooled OLS cautiously—it ignores the panel structure and may bias results.
• For machine learning models, consider feature engineering time lags or entity-based variables.

3. Sampling Strategy:

• Don’t treat panel data like a standard time-series (unless analyzing one entity).
• Instead, split your data entity-wise and time-aware, ensuring that the temporal order is preserved for each entity.
• Avoid shuffling across timeframes, as it breaks temporal dependencies.

4. R Packages for Panel Data:

• plm: Most popular for linear panel models (FE, RE, etc.).
• lme4: For mixed-effects models.
• data.table or dplyr: For managing large panel datasets efficiently.
• caret, mlr3, or tidymodels: For integrating machine learning models (with careful sampling).

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Bonus Tip:

If you’re working on machine learning models and looking for project ideas tailored to panel data or time-based datasets, check out this curated list of Machine Learning Projects for Final Year. It includes practical applications that often deal with temporal and cross-sectional patterns—perfect for students and researchers alike.

[supermarco]

Thank you for your interesting response! I m trying to use recipe()workflow() etc. on several ML models...

[supermarco]

Hello

I would like help please to run a ML through a workflow but i receive error message that i think is related to the pipe |>
(error message: The pipe operator requires a function call as RHS (:8:1)) that i replaced by %>%
but still receive other error messages. Below is an example that i would ask for help to correct with other ML like SVM, xgboost etc if you have suggestions. Thank you.

y <- rnorm(100, 7)
x1 <- rnorm(100, 6)
x2 <- rnorm(100, 8)

df <- data.frame(y, x1, x2)
df_split <- initial_split(df)
train_df <- training(df_split)
eq <- formula(y ~ x1 + x2)
rec1 <- recipe(eq, data = df) %>%
step_normalize(all_predictors())

model_1 <- gbm( distribution = "gaussian", n.tree=2, cv.folds=2 ) %>%
set_mode("regression") %>%
set_engine("gbm")
wf_1 <- workflow() %>%
add_recipe(rec1) %>%
add_model(model_1) %>%
fit(data = train_df)