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library(DT)
# --- Mock Data ---
model_data <- data.frame(
Methodology = c(
"Linear Regression",
"Logistic Regression",
"Random Forest",
"Gradient Boosting (XGBoost)",
"Support Vector Machine",
"Neural Network (MLP)",
"K-Nearest Neighbors",
"LASSO Regression",
"Decision Tree",
"Naive Bayes"
),
Category = c(
"Regression", "Classification", "Ensemble", "Ensemble",
"Kernel-based", "Deep Learning", "Instance-based",
"Regression", "Tree-based", "Probabilistic"
),
Interpretability = c(
"High", "High", "Medium", "Low",
"Low", "Low", "Medium",
"High", "High", "High"
),
Scalability = c(
"High", "High", "Medium", "High",
"Low", "High", "Low",
"High", "Medium", "High"
),
Handles_Nonlinearity = c(
"No", "No", "Yes", "Yes",
"Yes", "Yes", "Yes",
"No", "Yes", "No"
),
Typical_Use_Case = c(
"Continuous outcome prediction",
"Binary/multi-class classification",
"Tabular data, feature importance",
"Competitions, structured data",
"High-dimensional classification",
"Complex pattern recognition",
"Recommendation, anomaly detection",
"Feature selection, sparse models",
"Simple rule-based decisions",
"Text classification, spam filtering"
),
Requires_Feature_Scaling = c(
"No", "No", "No", "No",
"Yes", "Yes", "Yes",
"No", "No", "No"
),
stringsAsFactors = FALSE
)
# --- UI ---
ui <- fluidPage(
titlePanel("Model / Methodology Comparison"),
sidebarLayout(
sidebarPanel(
width = 3,
h4("Filters"),
selectInput(
"category_filter", "Category:",
choices = c("All", sort(unique(model_data$Category))),
selected = "All"
),
selectInput(
"interp_filter", "Interpretability:",
choices = c("All", "High", "Medium", "Low"),
selected = "All"
),
checkboxInput("nonlinear_only", "Only models handling nonlinearity", FALSE),
hr(),
p("This table summarizes key properties of common modeling methodologies.",
style = "color: grey; font-size: 0.9em;")
),
mainPanel(
width = 9,
DTOutput("model_table")
)
)
)
# --- Server ---
server <- function(input, output, session) {
filtered_data <- reactive({
df <- model_data
if (input$category_filter != "All") {
df <- df[df$Category == input$category_filter, ]
}
if (input$interp_filter != "All") {
df <- df[df$Interpretability == input$interp_filter, ]
}
if (input$nonlinear_only) {
df <- df[df$Handles_Nonlinearity == "Yes", ]
}
df
})
output$model_table <- renderDT({
datatable(
filtered_data(),
options = list(
pageLength = 10,
autoWidth = TRUE,
columnDefs = list(
list(width = "180px", targets = 0),
list(width = "200px", targets = 5)
)
),
rownames = FALSE,
colnames = c(
"Methodology", "Category", "Interpretability", "Scalability",
"Handles Nonlinearity", "Typical Use Case", "Requires Feature Scaling"
)
)
})
}
shinyApp(ui, server) |