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## 노트

### 말뭉치

1. 이를 Gradient boosting tree라고도 하는데, 구현한 대표적인 라이브러리로 XGboost를 들 수 있습니다.[1]
2. 또한 residual fitting model은 gradient boosting 모델의 한 가지 종류입니다.[1]
3. Like Random Forest, Gradient Boosting is another technique for performing supervised machine learning tasks, like classification and regression.[2]
4. The implementations of this technique can have different names, most commonly you encounter Gradient Boosting machines (abbreviated GBM) and XGBoost.[2]
5. How Gradient Boosting works Let’s look at how Gradient Boosting works.[2]
6. When we train each ensemble on a subset of the training set, we also call this Stochastic Gradient Boosting, which can help improve generalizability of our model.[2]
8. See Can Gradient Boosting Learn Simple Arithmetic?[4]
9. The gradient boosting algorithm (gbm) can be most easily explained by first introducing the AdaBoost Algorithm.[5]
11. The major difference between AdaBoost and Gradient Boosting Algorithm is how the two algorithms identify the shortcomings of weak learners (eg. decision trees).[5]
12. I hope that this article helped you to get a basic understanding of how the gradient boosting algorithm works.[5]
13. Like other boosting methods, gradient boosting combines weak "learners" into a single strong learner in an iterative fashion.[6]
14. Now, let us consider a gradient boosting algorithm with M {\displaystyle M} stages.[6]
15. Gradient boosting is typically used with decision trees (especially CART trees) of a fixed size as base learners.[6]
16. Gradient boosting can be used in the field of learning to rank.[6]
17. Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications.[7]
18. This article gives a tutorial introduction into the methodology of gradient boosting methods with a strong focus on machine learning aspects of modeling.[7]
19. A theoretical information is complemented with descriptive examples and illustrations which cover all the stages of the gradient boosting model design.[7]
20. This formulation of boosting methods and the corresponding models were called the gradient boosting machines.[7]
21. Gradient boosting machines (GBMs) are an extremely popular machine learning algorithm that have proven successful across many domains and is one of the leading methods for winning Kaggle competitions.[8]
22. Many gradient boosting applications allow you to “plug in” various classes of weak learners at your disposal.[8]
23. Gradient boosting is a machine learning technique for regression and classification problems that produce a prediction model in the form of an ensemble of weak prediction models.[9]
24. Gradient boosting basically combines weak learners into a single strong learner in an iterative fashion.[9]
25. Gradient boosting is applicable to many different risk functions and optimizes prediction accuracy of those functions, which is an advantage to conventional fitting methods.[9]
26. In this paper, we employ a gradient boosting regression tree method (GBM) to analyze and model freeway travel time to improve the prediction accuracy and model interpretability.[10]
27. The gradient boosting tree method strategically combines additional trees by correcting mistakes made by its previous base models, therefore, potentially improves prediction accuracy.[10]
28. Gradient boosting is a technique used in creating models for prediction.[11]
29. The concept of gradient boosting originated from American statistician, Leo Breiman, who discovered that the technique could be applied on appropriate cost functions as an optimization algorithm.[11]
30. One popular regularization parameter is M, which denotes the number of iterations of gradient boosting.[11]
31. A larger number of gradient boosting iterations reduces training set errors.[11]
32. This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models.[12]
33. Gradient boosting can be used for regression and classification problems.[12]
34. Don't just take my word for it, the chart below shows the rapid growth of Google searches for xgboost (the most popular gradient boosting R package).[13]
35. From data science competitions to machine learning solutions for business, gradient boosting has produced best-in-class results.[13]
36. Gradient boosting is a type of machine learning boosting.[13]
37. The name gradient boosting arises because target outcomes for each case are set based on the gradient of the error with respect to the prediction.[13]
38. The term gradient boosting consists of two sub-terms, gradient and boosting.[14]
40. Gradient boosting re-defines boosting as a numerical optimisation problem where the objective is to minimise the loss function of the model by adding weak learners using gradient descent.[14]
41. Gradient boosting does not modify the sample distribution as weak learners train on the remaining residual errors of a strong learner (i.e, pseudo-residuals).[14]
42. Although most of the Kaggle competition winners use stack/ensemble of various models, one particular model that is part of most of the ensembles is some variant of Gradient Boosting (GBM) algorithm.[15]
43. I am going to explain the pure vanilla version of the gradient boosting algorithm and will share links for its different variants at the end.[15]
44. Let’s see how maths work out for Gradient Boosting algorithm.[15]
45. The logic behind gradient boosting is simple, (can be understood intuitively, without using mathematical notation).[15]
46. Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model.[16]
47. Decision trees are usually used when doing gradient boosting.[16]
48. The idea behind "gradient boosting" is to take a weak hypothesis or weak learning algorithm and make a series of tweaks to it that will improve the strength of the hypothesis/learner.[16]
49. Gradient boosting classifiers are the AdaBoosting method combined with weighted minimization, after which the classifiers and weighted inputs are recalculated.[16]
50. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data.[17]
51. Gradient boosting on decision trees is a form of machine learning that works by progressively training more complex models to maximize the accuracy of predictions.[18]
52. Gradient boosting is particularly useful for predictive models that analyze ordered (continuous) data and categorical data.[18]
53. Gradient boosting benefits from training on huge datasets.[18]
54. Let’s look more closely at our GPU implementation for a gradient boosting library, using CatBoost as the example.[18]
55. Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration.[19]
56. It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure.[19]
57. Gradient boosting falls under the category of boosting methods, which iteratively learn from each of the weak learners to build a strong model.[20]
58. The term "Gradient" in Gradient Boosting refers to the fact that you have two or more derivatives of the same function (we'll cover this in more detail later on).[20]
59. Over the years, gradient boosting has found applications across various technical fields.[20]
61. Gradient boosting machines (GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work.[21]
62. We finish off by clearing up a number of confusion points regarding gradient boosting.[21]
63. Hence, in the second part, we leverage the benchmarking results and develop a method based on Gradient Boosting Decision Tree (GBDT) to estimate the time-saving for any given task merging case.[22]
64. Gradient Boosting is a popular boosting algorithm.[23]
65. In gradient boosting, each predictor corrects its predecessor’s error.[23]
66. 3 Training (solid lines) and validation (dashed lines) errors for standard gradient boosting (red) and AGB (blue) for Model 1 (left) and Model 5 (right).[24]
67. As it is generally the case for gradient boosting (e.g., Ridgeway 2007), the validation error decreases until predictive performance is at its best and then starts increasing again.[24]
68. However, AGB outperforms gradient boosting in terms of number of components of the output model, which is much smaller for AGB.[24]
69. For an end-to-end walkthrough of training a Gradient Boosting model check out the boosted trees tutorial.[25]
70. A Gradient Boosting Machine or GBM combines the predictions from multiple decision trees to generate the final predictions.[26]
71. Extreme Gradient Boosting or XGBoost is another popular boosting algorithm.[26]
72. There are a lot of resources online about gradient boosting, but not many of them explain how gradient boosting relates to gradient descent.[27]
73. At each iteration of the gradient boosting procedure, we train a base estimator to predict the gradient descent step.[27]
74. Gradient Boosting is a machine learning algorithm, used for both classification and regression problems.[28]
75. In gradient boosting decision trees, we combine many weak learners to come up with one strong learner.[28]
76. One problem that we may encounter in gradient boosting decision trees but not random forests is overfitting due to the addition of too many trees.[28]
77. Till now, we have seen how gradient boosting works in theory.[28]