Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.
More details about the implementation can be found in this technical note:
https://www.researchgate.net/publication/392923564_Backpropagating_quasi-randomized_neural_networks
On GitHub:
https://github.com/Techtonique/tisthemachinelearner
!pip install tisthemachinelearner
%load_ext rpy2.ipython
%%R
install.packages("reticulate")
library(reticulate)
library(MASS)
# 2. Import Python modules (with convert = FALSE for fine control)
sklearn_metrics <- import("sklearn.metrics", convert = TRUE)
tisthemachinelearner <- import("tisthemachinelearner")
%%R
df <- read.csv("https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/regression/boston_dataset2.csv")
%%R
head(df)
V0 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 target
1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2
6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7
training_index
1 0
2 1
3 0
4 1
5 1
6 1
%%R
# 3. Prepare Boston dataset
idx <- which(df$training_index == 1)
X_train <- as.matrix(df[idx, seq_len(13)])
y_train <- df$target[idx]
X_test <- as.matrix(df[-idx, seq_len(13)])
y_test <- df$target[-idx]
%%R
# 5. Build and train model
model <- tisthemachinelearner$FiniteDiffRegressor(
base_model = "GradientBoostingRegressor",
lr = 0.01,
optimizer = "gd"
)
# Convert R matrices/vectors to Python numpy arrays
X_train_py_list <- reticulate::r_to_py(X_train)
y_train_py_list <- reticulate::r_to_py(y_train)
X_test_py_list <- reticulate::r_to_py(X_test)
y_test_py_list <- reticulate::r_to_py(y_test)
# Pass Python numpy arrays to the Python model
model$fit(X_train_py_list, y_train_py_list, epochs=5L, show_progress = TRUE)
# 6. Predict and evaluate
y_pred <- model$predict(X_test_py_list)
# Ensure y_test_py (the Python object) is used, not y_test (the R vector)
# Now we should also convert y_test to a Python numpy array for the metric calculation
mse <- sqrt(sklearn_metrics$mean_squared_error(y_test_py_list, y_pred))
# 7. Display results
cat("Test MSE:", mse, "\n")
Test MSE: 2.771768
%%R
library(ggplot2)
# Create a dataframe for plotting
plot_data <- data.frame(Observed = y_test, Predicted = py_to_r(y_pred))
# Generate the scatterplot
ggplot(plot_data, aes(x = Observed, y = Predicted)) +
geom_point(alpha = 0.6) + # Use some transparency for overlapping points
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y=x line
theme_minimal() + # Use a minimal theme
labs(title = "Observed vs. Predicted Values",
x = "Observed",
y = "Predicted")
And here is a link to the Python notebook containing examples of use of FiniteDiffRegressor:
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