参数解释: B:回归系数,是个向量(“the vector B of regression coefficients in the linear model Y = X*B”)。 BINT:回归系数的区间估计(“a matrix BINT of 95% confidence intervals for B”)。 R:残差( “a vector R of residuals”)。 RINT:置信区间(“a matrix RINT of intervals that can be...
rng(5);% For reproducibility% Set aside 90% of the data for trainingcv = cvpartition(height(housing),'holdout',0.1); t = RegressionTree.template('MinLeaf',5); mdl = fitensemble(X(cv.training,:),y(cv.training,:),'LSBoost',500,t,...'PredictorNames',inputNames,'ResponseName',output...
We have already learned aboutgradient boostinganddecision trees. As a consequence, it will be easy to understand the definition of a boosted tree. A boosted tree is an additive model obtained from a gradient boosting algorithm in which decision trees (or regression trees) are used as base learn...
Regression trees can perform better when using the random forest method. The regression tree can be boosted using these six stages. First, a subset is created from the original dataset. Initially, the weights of all the data points are equal. A foundation model is constructed using this subset...
least squares regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. (Wikipedia) ...
Train a Regression Tree Using the Housing Data rng(5);% For reproducibility% Set aside 90% of the data for trainingcv = cvpartition(height(housing),'holdout',0.1); t = RegressionTree.template('MinLeaf',5); mdl = fitensemble(X(cv.training,:),y(cv.training,:),'LSBoost',500,t,......