Questions tagged [gradient-boosting]

For questions related to gradient boosting, which is a machine learning technique that can be used for regression and classification problems and which produces a prediction model in the form of an ensemble of other smaller prediction models (typically decision trees).

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Multi-Variate Time-Series forecasting with XGBoost

I have trained an XGBoost model on a time-series dataset for predicting a value. The time series has 5 features and one label (the target value). The trained model works fine on both training and ...
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Expected Revenue Using Gradient Boost for Regression

I have trained a ML algorithm (gradient boost) to do regression on banana prices, such that I can guess the selling price of any given banana. Using scikit's regression boost algorithm, I am able to ...
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Tree boosting additive loss

In the XGBoost documentation, they specify that the additive training is done given an objective $obj^{(t)}$ defined as $obj^{(t)} = \sum\limits_{i=1}^n \ell(y_i, \hat{y}_i^{(t-1)}+f_t(x_i)) + \sum\...
2 votes
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134 views

Why is the exponential loss used in this case?

I am reading the paper Tracking-by-Segmentation With Online Gradient Boosting Decision Tree. In Section 2.1, the paper says Given training examples, $\left\{\left(\mathbf{x}_{i}, y_{i}\right) \mid \...
2 votes
1 answer
44 views

House price inflation modelling

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 ...
1 vote
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React on train-validation curve after trening

I have a regression task that I tray to solve with AI. I have around 6M rows with about 30 columns. (originally there was 100, but I reduce it with drop feature importance) I understand basic ...
3 votes
0 answers
127 views

Can XGBoost solve XOR problem?

I've read that decision trees are able to solve XOR operation so I conclude that XGBoost algorithm can solve it as well. But my tests on the datasets (datasets that should be highly "xor-ish"...
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1 vote
1 answer
81 views

Has "deep vs. wide" been resolved?

All else being equal, including total neuron count, I give the following definitions: wide is a parallel ensemble, where good chunks of the neurons have the same inputs because the inputs are shared ...
3 votes
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When do the ensemble methods beat neural networks?

In many applications and domains, computer vision, natural language processing, image segmentation, and many other tasks, neural networks (with a certain architecture) are considered to be by far the ...
5 votes
3 answers
639 views

How do weak learners become strong in boosting?

Boosting refers to a family of algorithms which converts weak learners to strong learners. How does it happen?
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2 votes
1 answer
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What are some applications where tree models perform better than neural networks?

Neural networks are known to be generally better modeling techniques as compared to tree-based models (such as decision trees). Are there any exceptions to this?
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How would the "best function" been constructed if there are no computationally limitations?

I am reading the Wikipedia article on gradient boosting. There is written: Unfortunately, choosing the best function $h$ at each step for an arbitrary loss function $L$ is a computationally ...
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How can I use gradient boosting with multiple features?

I'm trying to use gradient boosting and I'm using sklearn's GradientBoostingClassifier class. My problem is that I'm having a data frame with 5 columns and I want ...