Bootstrap Confidence Intervals in Python: How Certain Is Your Model’s Score?
Calculate bootstrap confidence intervals in Python and compare model errors with paired resampling. Includes tested code, a chart, and clear limitations.
Category: Articles
In this category you will find interesting articles about Machine Learning and Data Science, both written by us or fished online.
We have tried to cover a wide variety of topics, and we will keep posting here regularly, so be sure to follow us!
Permutation Feature Importance in Python: What Your Model Actually Uses
Learn permutation feature importance in Python with tested code, a grouped-feature example, and a clear explanation of correlated-feature pitfalls.
Learning Curves in Python: Will More Data Help Your Model?
Build learning curves in Python to investigate whether more training data will help. Includes tested scikit-learn code and a linear-versus-cubic comparison.
Probability Calibration in Python: Can You Trust Your Model’s Probabilities?
A friendly guide to probability calibration in Python, with a tested scikit-learn example, reliability diagram, and honest before-and-after results.
Classification Threshold Tuning in Python: Choose the Right Cutoff
Choose a classification threshold using validation data, error costs and a tested Python example. Compare the trade-offs on an untouched test set.
ROC AUC vs PR AUC: Imbalanced Data Explained with Python
Compare ROC AUC, PR AUC and average precision with a reproducible Python example showing how class imbalance changes precision.
Logistic Regression vs Random Forest vs Gradient Boosting: When the Simple Model Wins
On a real churn dataset, plain logistic regression (0.870) beat both random forest (0.830) and gradient boosting (0.840). Why it happened, and how to choose bet...
Overfitting vs Underfitting: The Two Numbers That Diagnose Your Model
Training error kept improving while test error exploded from 1.13 to 195.86. A hands-on guide to overfitting, underfitting and why your test set must be sealed ...
Precision vs Recall: How a 96% Accurate Model Missed Half the Cases
A classifier scored 96% accuracy and still missed 47% of the cases it existed to find. Here is how the confusion matrix, precision and recall expose what accura...
What Is Artificial General Intelligence (AGI)? Definitions, Predictions and the $100 Billion Debate
What is artificial general intelligence (AGI), and has it been achieved? Definitions, evolution, and what AI leaders predict.