to use XGBoost: for tabular data where you want the best possible performance

Boosting (machine learning)

Boosting reduces bias in ML models

UMAP is faster than t-SNE

UMAP is faster due to approximate nearest neighbors and cross-entropy optimization

to use a CNN: for data with spatial structure like images or time series

CNNs excel in recognizing patterns in spatially structured data such as images or time series

B-trees optimize: disk-based sorted data with O(log n) reads per query

B-trees optimize disk-based sorted data with O(log n) reads per query

KV-cache reduces redundant computation in autoregressive generation

KV-cache stores previously computed outputs to avoid redundant calculations in autoregressive models

LoRA (machine learning)

LoRA uses r << d for efficient adaptation