RegressionModels

enum RegressionModels : Enum<RegressionModels> , ConvertibleToJava<RegressionModels>

Enum for all Regression models supported by AutoML.

Entries

Link copied to clipboard

Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.

Link copied to clipboard

The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.

Link copied to clipboard

Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Link copied to clipboard

K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.

Link copied to clipboard

Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.

Link copied to clipboard

SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find the model parameters that correspond to the best fit between predicted and actual outputs. It's an inexact but powerful technique.

Link copied to clipboard

Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the "bagging" method. The general idea of the bagging method is that a combination of learning models increases the overall result.

Link copied to clipboard

Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.

Link copied to clipboard

LightGBM is a gradient boosting framework that uses tree based learning algorithms.

Link copied to clipboard

XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

Types

Link copied to clipboard
object Companion

Properties

Link copied to clipboard

Returns a representation of an immutable list of all enum entries, in the order they're declared.

Link copied to clipboard
val javaValue: RegressionModels
Link copied to clipboard
Link copied to clipboard

Functions

Link copied to clipboard
open override fun toJava(): RegressionModels
Link copied to clipboard

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Link copied to clipboard

Returns an array containing the constants of this enum type, in the order they're declared.