**Gradient Boosting Machine**or GBM combines the predictions from multiple decision trees to generate the final predictions. … So, every successive decision tree is built on the errors of the previous trees. This is how the trees in a gradient boosting machine algorithm are built sequentially.

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**Gradient boosting** refers to a class of ensemble machine learning algorithms that can be used for classification or regression predictive modeling problems. Gradient boosting is also known as gradient tree boosting, stochastic gradient boosting (an extension), and gradient boosting machines, or GBM for short.

Difference between GBM (Gradient Boosting Machine) and XGBoost (Extreme Gradient Boosting) The objective of both GBM and XGBoost is **to minimize the loss function**. Both are used to improve the performance of an algorithm using Ensemble Learning. Both are boosting algorithms.

Gradient boosting is a type of machine learning boosting. It relies on **the intuition that the best possible next model**, when combined with previous models, minimizes the overall prediction error. … If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero.

Overview. The gbm package, which stands for **generalized boosted models**, provides extensions to Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient boosting machine.

As we’ll see, A GBM is a **composite model that combines the efforts of multiple weak models to create a strong model**, and each additional weak model reduces the mean squared error (MSE) of the overall model. We give a fully-worked GBM example for a simple data set, complete with computations and model visualizations.

CatBoost is an algorithm for gradient boosting on decision trees. It is developed by **Yandex researchers and engineers**, and is used for search, recommendation systems, personal assistant, self-driving cars, weather prediction and many other tasks at Yandex and in other companies, including CERN, Cloudflare, Careem taxi.

2. Advantages of Light GBM. Faster training speed and higher efficiency: Light GBM **use histogram based algorithm** i.e it buckets continuous feature values into discrete bins which fasten the training procedure. Lower memory usage: Replaces continuous values to discrete bins which result in lower memory usage.

If you carefully tune parameters, **gradient boosting can result in better performance than random forests**. However, gradient boosting may not be a good choice if you have a lot of noise, as it can result in overfitting. They also tend to be harder to tune than random forests.

The residual is the gradient of loss function and the sign of the residual, , is the gradient of loss function . By adding in approximations to residuals, **gradient boosting machines are chasing gradients**, hence, the term gradient boosting.

- Choose a relatively high learning rate. …
- Determine the optimum number of trees for this learning rate. …
- Tune tree-specific parameters for decided learning rate and number of trees. …
- Lower the learning rate and increase the estimators proportionally to get more robust models.

4)Applications: i) Gradient Boosting Algorithm is generally used when we want to decrease the **Bias error**. ii) Gradient Boosting Algorithm can be used in regression as well as classification problems. In regression problems, the cost function is MSE whereas, in classification problems, the cost function is Log-Loss.

The random forest algorithm is actually **a bagging algorithm**: also here, we draw random bootstrap samples from your training set. However, in addition to the bootstrap samples, we also draw random subsets of features for training the individual trees; in bagging, we provide each tree with the full set of features.

This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems.

Gradient Boosting for regression. GB builds an additive model in a forward stage-wise fashion; it **allows for the optimization of arbitrary differentiable loss functions**. In each stage a regression tree is fit on the negative gradient of the given loss function.

Gradient boosting is **a machine learning technique used in regression and classification tasks**, among others. It gives a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees.

**Gradient Boosting Machine** (GBM) A Gradient Boosting Machine or GBM combines the predictions from multiple decision trees to generate the final predictions. Keep in mind that all the weak learners in a gradient boosting machine are decision trees.

In predictive modeling, boosting is **an iterative ensemble method that starts out by applying a classification algorithm and generating classifications**. The idea is to concentrate the iterative learning process on the hard-to-classify cases. …

The **gradient boosting algorithm** (gbm) can be most easily explained by first introducing the AdaBoost Algorithm. The AdaBoost Algorithm begins by training a decision tree in which each observation is assigned an equal weight. … Gradient Boosting trains many models in a gradual, additive and sequential manner.

Not only does it build one of the most accurate model on whatever dataset you feed it with — requiring minimal data prep — CatBoost also gives by far the **best open source interpretation tools available** today AND a way to productionize your model fast.

CatBoost uses **oblivious decision trees**, where the same splitting criterion is used across an entire level of the tree. Such trees are balanced, less prone to overfitting, and allow speeding up prediction significantly at testing time.

CatBoost is **the only boosting algorithm with very less prediction time**. Thanks to its symmetric tree structure. It is comparatively 8x faster than XGBoost while predicting.

The implementations of this technique can have different names, most commonly you encounter **Gradient Boosting machines** (abbreviated GBM) and XGBoost. XGBoost is particularly popular because it has been the winning algorithm in a number of recent Kaggle competitions.

Structural Differences in LightGBM & XGBoost LightGBM uses a novel technique of Gradient-based One-Side Sampling (GOSS) to filter out the data instances for finding a split value while XGBoost uses pre-sorted algorithm & Histogram-based algorithm for computing the best split.

XGBoost, which stands for Extreme Gradient Boosting, is a **scalable, distributed gradient-boosted decision tree (GBDT) machine learning library**. It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.

Bagging and **Boosting decrease the variance of a single estimate** as they combine several estimates from different models. As a result, the performance of the model increases, and the predictions are much more robust and stable.

Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally **improve the performance of the algorithm by reducing overfitting**.

If the classifier is stable and simple (high bias) then we should apply Boosting. Bagging is extended to Random forest model while **Boosting is extended to Gradient boosting**.

**AdaBoost** is a meta-algorithm, which means it can be used together with other algorithms for perfomance improvement. Indeed, the concept of boosting is a type of linear regression. Now, specifically answering your question, AdaBoost is actually intented for classification and regression problems.

Each tree attempts to minimize the errors of previous tree. Trees in boosting are weak learners but **adding many trees in series and each focusing on the errors** from previous one make boosting a highly efficient and accurate model. … Everytime a new tree is added, it fits on a modified version of initial dataset.

In a regression tree, a regression model **is fit to the target variable using each of the independent variables**. After this, the data is split at several points for each independent variable. At each such point, the error between the predicted values and actual values is squared to get “A Sum of Squared Errors”(SSE).

“Support Vector Machine” (SVM) is **a supervised machine learning algorithm** that can be used for both classification or regression challenges. However, it is mostly used in classification problems. … The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line).

interaction. **depth = 1** : additive model, interaction. depth = 2 : two-way interactions, etc. As each split increases the total number of nodes by 3 and number of terminal nodes by 2, the total number of nodes in the tree will be 3∗N+1 and the number of terminal nodes 2∗N+1.

It is a technique of producing an additive predictive model by combining various weak predictors, typically Decision Trees. Gradient Boosting Trees **can be used for both regression and classification**.

nodesize from R random forest package. **Minimum size of terminal nodes**. Setting this number larger causes smaller trees to be grown (and thus take less time).

**Bagging attempts to reduce the chance of overfitting complex models**. It trains a large number of “strong” learners in parallel. A strong learner is a model that’s relatively unconstrained. Bagging then combines all the strong learners together in order to “smooth out” their predictions.

Random Forest is one of the most popular and most powerful machine learning algorithms. It is a **type of ensemble machine learning algorithm** called Bootstrap Aggregation or bagging. … The Random Forest algorithm that makes a small tweak to Bagging and results in a very powerful classifier.

CatBoost vs LightGBM This time, we build CatBoost and LightGBM regression models on the California house pricing dataset. LightGBM has slightly outperformed CatBoost and it is **about 2 times faster than CatBoost**!

AdaBoost is the **first designed boosting algorithm with** a particular loss function. On the other hand, Gradient Boosting is a generic algorithm that assists in searching the approximate solutions to the additive modelling problem. This makes Gradient Boosting more flexible than AdaBoost.

XGBoost is easy to implement in scikit-learn. XGBoost is an ensemble, so it scores better than individual models. XGBoost is regularized, so default models often don’t overfit. … XGBoost **learns form its mistakes** (gradient boosting).