Gradient Boosting refers to a machine learning technique that builds predictive models by combining the outputs of multiple weak learners, typically decision trees, to create a strong predictive model. This skill involves iteratively training models on the residual errors of previous models, thereby improving accuracy and reducing bias. Knowledge of Gradient Boosting is used in various applications, including regression and classification tasks, to enhance model performance and achieve better predictive results.

Gradient Boosting Machines (GBM) refers to an ensemble machine learning technique that combines multiple weak predictive models, typically decision trees, to create a strong predictive model. It works iteratively by optimizing a loss function and correcting errors of previous models through weighted adjustments. GBM is used in regression, classification, and ranking tasks, excelling in handling structured data and achieving high predictive accuracy. By sequentially improving model performance, it enables robust and interpretable solutions for a wide range of applications.

K-Means Clustering is a machine learning technique used to group similar data points into clusters. It requires specialized skills and knowledge in data analysis to effectively implement and interpret the results.

Random Forest Algorithm refers to an ensemble learning method used for classification and regression tasks, which operates by constructing multiple decision trees during training and outputting the mode or mean prediction of the individual trees. This skill involves understanding the principles of bagging and feature randomness to improve model accuracy and control overfitting. Knowledge of Random Forest Algorithm is applied in various domains, including finance, healthcare, and marketing, to analyze complex datasets, make predictions, and derive insights from data.

Ridge/LASSO Regressions refer to statistical techniques used for linear regression analysis that incorporate regularization methods to prevent overfitting. These techniques apply penalties to the coefficients of the regression model, which helps in managing multicollinearity and improving model interpretability. Knowledge of Ridge/LASSO Regressions is utilized to enhance predictive accuracy in various fields, including finance, healthcare, and social sciences, by selecting relevant features and optimizing model performance.