Cross Validation
Cross-validation is a model evaluation technique that repeatedly splits a dataset into different training and validation subsets, trains and tests the model on each split, and averages the results to produce a more reliable estimate of how the model will perform on unseen data.
15 resources across 2 libraries
Glossary Terms(12)
Overfitting
Overfitting occurs when a machine learning model learns the training data too closely, including its noise and idiosyncrasies, resulting in poor performance on…
Confusion Matrix
A confusion matrix is a table that summarizes the performance of a classification model by showing the counts of correct and incorrect predictions broken down…
Precision and Recall
Precision and recall are two complementary metrics for evaluating a classification model: precision measures how many of the model's positive predictions were…
F1 Score
The F1 score is a single classification metric that combines precision and recall into one number by calculating their harmonic mean, providing a balanced meas…
ROC Curve
A ROC (Receiver Operating Characteristic) curve is a graph that plots a binary classification model's true positive rate against its false positive rate across…
Cross-Validation
Cross-validation is a model evaluation technique that repeatedly splits a dataset into different training and validation subsets, trains and tests the model on…
Hyperparameter Tuning
Hyperparameter tuning is the process of systematically searching for the configuration values that control a machine learning model's training process — such a…
AutoML
AutoML (Automated Machine Learning) refers to tools and techniques that automate parts of the machine learning pipeline — such as feature selection, model sele…
Early Stopping
Early stopping is a regularization technique that halts model training once performance on a held-out validation set stops improving, preventing the model from…
Grid Search
Grid search is a hyperparameter tuning method that exhaustively evaluates a model across every combination of hyperparameter values from a predefined discrete…
Random Search
Random search is a hyperparameter tuning method that samples a fixed number of hyperparameter combinations randomly from specified distributions or ranges, rat…
Bayesian Optimization
Bayesian optimization is a sample-efficient hyperparameter tuning method that builds a probabilistic surrogate model of how hyperparameters affect performance,…
Cheat Sheets(3)
LightGBM Cheat Sheet
LightGBM reference covering the scikit-learn and native Dataset APIs, leaf-wise tree growth, and the hyperparameters that control speed and overfitting.
CatBoost Cheat Sheet
CatBoost reference covering native categorical feature handling, the Pool data structure, ordered boosting, and built-in cross-validation.
Cross-Validation Cheat Sheet
Methods for reliably estimating model generalization performance, covering k-fold, stratified, time-series, and leave-one-out cross-validation with scikit-lear…