How do I improve the performance of my current GLMNET model

Hi All,

I'm in the early stages of my machine learning journey and need some help with a model I'm working on. I am trying to predict hourly bike rentals using a dataset from UCI Repository. The feature I am trying to predict is the "rented_count". I am testing a GLMNET Model and the best MAE from by cross validation is 371.92. I know there are more powerful models to explore, however I wanted to explore if there are ways to improve my current model. Are there additional preprocessing steps I can do to improve my current model?

Below is my code. Here are the steps in the preprocessing steps I did -

  1. Create a "month" feature from the "date"
  2. Create a "day of the week" feature from the "date"
  3. Create a "weekend/weekday" feature from the "date"

I noticed several features are skewed in the dataset (rented_count, dew_point, windspeed, etc). I tried including a "step_YeoJohnson" which I understand can be used to transform skewed data, however I was not sure how to interpret the MAE metric as it was also in the transformed format.

Please see below for my code. Any help will be appreciated.

# Load libraries
library(tidyverse)
library(tidymodels)
library(lubridate)

# Load dataset
seoul_bikes_data_path = "https://archive.ics.uci.edu/ml/machine-learning-databases/00560/SeoulBikeData.csv"

colnames = c("date", "rented_count", "hour", "temp", "humidity", "windspeed", "visibility", "dew_point", "solar_rad", 
             "rainfall", "snowfall", "season", "holiday", "functional_day")

seoul_bikes_tbl = read.csv(seoul_bikes_data_path, header = F, skip = 1) %>% 
    as_tibble()

colnames(seoul_bikes_tbl) = colnames

seoul_bikes_tbl2 <- seoul_bikes_tbl %>%

# PREPROCESSING STEPS
# Change date from chr to date format
    mutate(date = dmy(date)) %>% 
    
    # Get the month day and year from the date 
    mutate(month = month(date, label = T)) %>% 
    mutate(year = year(date)) %>% 
    mutate(day = wday(date, label = T)) %>% 
    
    # Create a month feature such ase "Dec - 2017"
    mutate(month = str_glue("{month} - {year}")) %>% 
    
    # Convert chr features to factors and relevel factors for the "month" feature
    mutate(month = as.character(month)) %>% 
    mutate_if(is.character, as.factor) %>% 
    mutate(day = as.character(day)) %>% 
    mutate(day = as.factor(day)) %>% 
    mutate(month = month %>% fct_relevel("Dec - 2017", "Jan - 2018", "Feb - 2018", "Mar - 2018", "Apr - 2018", "May - 2018",
                                         "Jun - 2018", "Jul - 2018", "Aug - 2018", "Sep - 2018", "Oct - 2018", "Nov - 2018")) %>% 
    mutate(hour = as.factor(hour)) %>% 
    
    # Remove unwanted columns
    select(-date, -year) %>% 
    mutate(weekend = case_when(
        day == "Sat" | day == "Sun" ~ "Weekend", TRUE ~ "Weekday"
    )) %>% 
    
    # Change "weekend" feature to factor
    mutate(weekend = as.factor(weekend))

# MODELLING
# Split Data
set.seed(100)
seoul_initial_split <- initial_split(seoul_bikes_tbl2, prop = 0.80)

seoul_train_tbl = training(seoul_initial_split)
seoul_test_tbl = testing(seoul_initial_split)

# Cross Validation Specs
set.seed(101)
seoul_folds <- vfold_cv(seoul_train_tbl, v = 10, repeats = 2)


# Recipe
seoul_recipe = recipe(rented_count ~ ., data = seoul_train_tbl) %>% 
    step_zv(all_predictors()) %>% 
    step_dummy(all_nominal()) %>% 
    prep()

# Glmnet Model Spec
seoul_glmnet_model <- linear_reg(mode = "regression",
                                 penalty = tune(),
                                 mixture = tune()) %>% 
    set_engine("glmnet")

# Workflow
seoul_glmnet_wf <- 
    workflow() %>% 
    add_model(seoul_glmnet_model) %>% 
    add_recipe(seoul_recipe)

# Create tuning grid
seoul_glmnet_params <- parameters(penalty(), mixture())

set.seed(103)
seoul_glmnet_tune_grid <- grid_max_entropy(seoul_glmnet_params, size = 20)


# Tune Hyper-parameters
seoul_glmnet_tune_results <- 
    tune_grid(object    = seoul_glmnet_wf,
              resamples = seoul_folds,
              grid      = seoul_glmnet_tune_grid,
              metrics   = metric_set(mae, mape, rmse, rsq),
              control   = control_grid(verbose = TRUE)
    )

# Show Best MAE
seoul_glmnet_tune_results %>% show_best("mae", n = 10, maximize = FALSE)
  • Looking at your outcome, I would think about a sqrt or log(x + 1) transformation of the data (or perhaps use Poisson regression with inflated zeros).

  • Don't make numeric data qualitative. It has less information that way. See the next point

  • Examine if there are nonlinear relationships in your data and, for any you find, use the spline steps to model them that way. For example, when you treat hour as numeric, look at the nonlinear pattern of:

ggplot(seoul_bikes_tbl, aes(hour, sqrt(rented_count))) + 
  geom_point(alpha = .1) + 
  geom_smooth(se = FALSE)

  • Look for interactions by looking at smooths with facets. If the patterns are different, this suggests that you need an interaction. For example:
ggplot(seoul_bikes_tbl, aes(temp, sqrt(rented_count))) + 
  geom_point(aes(col = functional_day), alpha = .4) + 
  facet_wrap(~ season) + 
  geom_smooth(se = FALSE)

  • Should the functional_day = "No" be in the dataset? Does that mean that they are open? If so, get rid of them. The are the only reason for the zeros and that would make the modeling easier (and why would you predict when they are closed?).
3 Likes

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.

@Max Thank you. I will implement your suggestions.