The general gradient boosting algorithm for tree-based classifiers is as follows: Input: training set $\{(x_{i},y_{i})\}_{i=1}^{n}$, a differentiable loss function $L(y,F(x))$, and a number of iterations $M$. Algorithm: Initialize model with a constant value: $\displaystyle F_{0}(x)={\underset {\gamma }{\arg \min }}\sum _{i=1}^{n}L(y_{i},\gamma )$. For m = 1 to M: Compute so-called pseudo-residuals: $$r_{im}=-\left[{\frac {\partial L(y_{i},F(x_{i}))}{\partial F(x_{i})}}\right]_{F(x)=F_{m-1}(x)}\quad {\mbox{for }}i=1,\ldots ,n.$$ Fit a base learner (or weak learner, e.g. tree) $\displaystyle h_{m}(x)$ to pseudo-residuals, i.e. train it using the training set $\{(x_{i},r_{im})\}_{i=1}^{n}$. Compute multipliers …
I have a data set of house prices and their corresponding features (rooms, meter squared, etc). An additional feature is the sold date of the house. The aim is to create a model that can estimate the price of a house as if it was sold today. For example a house with a specific set of features (5 rooms, 100 meters squared) and today's date (28-1-2020), what would it sell for? Time is an important component, because prices increase (inflate …
For an application, I am using a Gradient boosting Tree based quantile regression model (LightGBM, Catboot) to predict the 5th percentile of the target variable. The model predicts point estimates, but I want to attach confidence in which the model predicts quantile value. I read some of the recent research - NGBoost (https://stanfordmlgroup.github.io/projects/ngboost/) - Used for Regression tasks Uncertainty prediction with Gradient Boosting Trees (https://arxiv.org/pdf/2006.10562.pdf) - Also for regression tasks. Is there a way to attach a confidence(probability) value with …
I am using gradient Gradient Boosted Trees (with Catboost) for a Regression task. Can GBtrees predict a label that is below the minimum (or above the max) that was seen in the training ? For instance if the minimum value the label had is 10, would GBtrees be able to predict 5?
I would like to train my datasets in scikit-learn but export the final Gradient Boosting Regressor elsewhere so that I can make predictions directly on another platform. I am aware that we can obtain the individual decision trees used by the regressor by accessing regressor.estimators[].tree_. What I would like to know is how to fit these decision trees together to make the final regression predictor.
I wonder whether algorithms such as GBM, XGBoost, CatBoost, and LightGBM perform more than two splits at a node in the decision trees? Can a node be split into 3 or more branches instead of merely binary splits? Can more than one feature be used in deciding how to split a node? Can a feature be re-used in splitting a descendant node?
I'm inspecting the weak estimators of my GradientBoostingClassifier model. This model was fit on a binary class dataset. I noticed that all the weak estimators under this ensemble classifier are decision tree regressor objects. This seems strange to me intuitively. I took the first decision tree in the ensemble and used it to predict independently on my entire dataset. The unique answers from the dataset were the following: array([-2.74, -1.94, -1.69, ...]) My question is: why and how does the …
XGBoost and standard gradient boosting train learners to fit the residuals rather than the observations themselves. I understand that this aspect of the algorithm matches the boosting mechanism which allows it to iteratively fit errors made by previous learners. Which other algorithms or also train single or multiple learners to fit residuals? Does this method only make sense for learners built in a sequence? Or also for any ensemble methods? Is there a deep significance to fitting residuals or is …
I am working on the Kaggle home loan model and interestingly enough, the GradientBoostClassifier has a considerably better score than XGBClassifier. At the same time it seems to not overfit as much. (note, I am running both algos with default settings). From what I've been reading XGBClassifier is the same as GradientBoostClassifier, just much faster and more robust. Therefore I am now confused on why would XGB overfit so much more than GradientBoostClassifier, when it should do the contrary? What …
Recently I have been doing some research on NGBoost, but I could not see any parameter for categorical features. Is there any parameter that I missed? __init__(self, Dist=<class 'ngboost.distns.normal.Normal'>, Score=<class 'ngboost.scores.MLE'>, Base=DecisionTreeRegressor(ccp_alpha=0.0, criterion='friedman_mse', max_depth=3, | max_features=None, max_leaf_nodes=None, | min_impurity_decrease=0.0, min_impurity_split=None, | min_samples_leaf=1, min_samples_split=2, | min_weight_fraction_leaf=0.0, presort='deprecated', | random_state=None, splitter='best'), natural_gradient=True, n_estimators=500, learning_rate=0.01, minibatch_frac=1.0, verbose=True, verbose_eval=100, tol=0.0001) https://github.com/stanfordmlgroup/ngboost