Tag: mllib
8 articles
Spark MLlib GBDT Algorithm: Gradient Boosting Principles,...
This article introduces the principles and applications of gradient boosting tree (GBDT) algorithm. First explains boosting tree basic concept through simple examples, then details algorithm flow i...
Spark MLlib Ensemble Learning: Random Forest, Bagging and...
This article systematically introduces ensemble learning methods in machine learning. Main content includes: 1) Basic definition and classification of ensemble...
Spark MLlib Decision Tree Pruning: Pre-pruning, Post-prun...
This article systematically introduces decision tree pre-pruning and post-pruning principles, compares core differences between three mainstream algorithms...
Spark MLlib Decision Tree: Classification Principles, Gin...
This article introduces the basic concepts, classification principles, and classification principles of decision trees. Decision tree is a non-linear...
Spark MLlib Logistic Regression: Input Function, Sigmoid,...
This article introduces the basic principles, application scenarios, and implementation in Spark MLlib of logistic regression. Logistic regression is an efficient binary classification algorithm wi...
Spark MLlib Linear Regression: Scenarios, Loss Function a...
Linear regression uses regression equations to model relationships between independent and dependent variables. This article covers regression scenarios (house...
Spark MLlib Logistic Regression: Sigmoid, Loss Function a...
Logistic regression is a classification model in machine learning — an efficient binary classification algorithm widely used in ad click-through rate...
Spark MLlib Linear Regression: Scenarios, Loss Function a...
Linear Regression is an analytical method that uses regression equations to model the relationship between one or more independent variables and a dependent...