Incompatibility between Training and Test Data in a Logistic Regression Model in R

Introduction

Logistic regression is a popular machine learning algorithm used for binary classification problems. It is widely employed in various fields, including medicine, finance, and marketing. When building a logistic regression model, it’s essential to consider the quality of the data used for training and testing. In this article, we’ll explore the issue of incompatibility between training and test data in a logistic regression model in R.

Background

Logistic regression is based on the assumption that the relationship between the independent variables (predictors) and the dependent variable (outcome) can be modeled using a linear equation with an added logit function. The goal is to predict the probability of a binary outcome (e.g., death within 72 hours). When training a logistic regression model, the data is typically split into training and test sets. The training set is used to estimate the model parameters, while the test set is used to evaluate its performance.

Data Preprocessing

In this problem, we have a dataset with 23 variables and 365 observations. To create a train/test split, we used the initial_split function from the rsample package. We also imputed missing values using the mice package. The code for these steps is shown below:

# Create a train/test split
mort_72_data <- initial_split(predict_mortality_changes72,
                              prop = 0.8, 
                              strata = die_in_72)

# Impute missing values
imputed_mort_72 <- mice(data = mort_72_data_train,
                        method = 'pmm', 
                        m = 100)
mort_72_data_train_imputed <- complete(imputed_mort_72)

Training a Logistic Regression Model

We trained a logistic regression model using three relevant variables identified by lasso regression. The code for this step is shown below:

# Train a logistic regression model
lr_model_01 <- glm(data = mort_72_data_train_imputed,
                   formula = die_in_72 ~ 
                     pfr_change_absolute +
                     apache_ii +
                     time_between_abg,
                   family = 'binomial')

Making Predictions

We made predictions on the training data using the predict function. The code for this step is shown below:

# Make predictions on the training data
pred_model_01 <- select(mort_72_data_train, die_in_72)
pred_model_01$prediction_response <- predict(object = lr_model_01,
                                             newdata = mort_72_data_train_imputed,
                                             type = 'response')

Error Message

When we tried to make predictions on the testing data using the predict function again, we encountered an error message indicating incompatibility between the training and test data. The error message is shown below:

Error:
! Assigned data `predict(object = lr_model_01, newdata = mort_72_data_test, type = "response")` must be compatible with existing data.
✖ Existing data has 291 rows.
✖ Assigned data has 74 rows.
ℹ Only vectors of size 1 are recycled.
Backtrace:
  1. base::`$<-`(`*tmp*`, prediction_response_2, value = `<dbl>`)
 12. tibble (local) `<fn>`(`<vctrs___>`)

Why Incompatibility Occurs

The incompatibility between the training and test data occurs because the number of rows in the assigned data (mort_72_data_test) does not match the number of rows in the existing data (mort_72_data_train). The predict function expects both the training and test data to have the same number of rows.

Solution

To solve this issue, we need to make sure that both the training and test data have the same number of rows. One way to do this is to use a technique called data augmentation. Data augmentation involves adding new observations to the existing data without changing any of the existing variables. This can help balance the distribution of the data and reduce overfitting.

Another approach is to use cross-validation techniques, such as k-fold cross-validation, to evaluate the model’s performance on multiple subsets of the data. This can help estimate the model’s generalization performance more accurately.

Conclusion

In conclusion, incompatibility between training and test data in a logistic regression model in R occurs when the number of rows in the assigned data does not match the number of rows in the existing data. To solve this issue, we need to ensure that both datasets have the same number of rows or use techniques like data augmentation and cross-validation.

References

• “Logistic Regression” by scikit-learn.org
• “Data Augmentation for Imbalanced Datasets” by arxiv.org
• “K-Fold Cross Validation” by scikit-learn.org

Last modified on 2024-09-10