Kernel Function
/ˈkɜːr.nəl ˈfʌŋk.ʃən/
noun … “measuring similarity in disguise.”
Kernel Trick
/ˈkɜːr.nəl trɪk/
noun … “mapping the invisible to the visible.”
Gradient Boosting
/ˈɡreɪ.di.ənt ˈbuː.stɪŋ/
noun … “learning from mistakes, one step at a time.”
Random Forest
/ˈrændəm fɔːrɪst/
noun … “many trees, one wise forest.”
Information Gain
/ˌɪn.fərˈmeɪ.ʃən ɡeɪn/
noun … “measuring how much a split enlightens.”
Information Gain is a metric used in decision tree learning and other machine learning algorithms to quantify the reduction in uncertainty (entropy) about a target variable after observing a feature. It measures how much knowing the value of a specific predictor improves the prediction of the outcome, guiding the selection of the most informative features when constructing decision trees, such as Decision Trees.
Logistic Regression
/ˈlɒdʒ.ɪ.stɪk rɪˈɡrɛʃ.ən/
noun … “predicting probabilities with a curve, not a line.”
Lasso Regression
/ˈlæs.oʊ rɪˈɡrɛʃ.ən/
noun … “OLS with selective pruning.”
Ridge Regression
/rɪdʒ rɪˈɡrɛʃ.ən/
noun … “OLS with a leash on wild coefficients.”
Ordinary Least Squares
/ˈɔːr.dən.er.i liːst skwɛərz/
noun … “fitting a line to tame the scatter.”
Ordinary Least Squares (OLS) is a fundamental method in statistics and regression analysis used to estimate the parameters of a linear model by minimizing the sum of squared differences between observed outcomes and predicted values. It provides the best linear unbiased estimates under classical assumptions, allowing analysts to quantify relationships between predictor variables and a response variable while assessing the strength and direction of these relationships.
SARIMA
/sɛˈriː.mə/
noun … “ARIMA with a seasonal compass.”