What XGBoost is used for?

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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems fast and accurately.

What is the XGBoost algorithm?

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XGBoost is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm, which attempts to accurately predict a target variable by combining the estimates of a set of simpler and weaker models.

Is XGBoost a classification or regression?

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XGBoost (eXtreme Gradient Boosting) is a popular supervised-learning algorithm used for regression and classification on large datasets.

Is XGBoost better than Random Forest?

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Since the gradient of the data is considered for each tree, XGBoost is faster, and the precision is more accurate than Random Forest. This makes developers to depend on XGBoost than Random Forest. XGBoost is more complex than any other decision tree algorithm.

Is XGBoost better than SVM?

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SVM and XGBoost models are developed for modeling global solar radiation. The two algorithms show comparable prediction accuracy. However, XGBoost models are more stable and efficient than SVM algorithms.

How can I run XGBoost on GPU?

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XGBoost uses NVIDIA's CUDA parallel computing platform. You need to install the CUDA toolkit and XGBoost with CUDA support, then enable training of an XGBoost model using the GPU is straightforward—set the hyperparameter tree_method to "gpu_hist."

When to Use XGBoost GPU Servers?

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1. Classification problems, especially those related to real-world business problems.
2. Problems in which the range or distribution of target values present in the training set can be expected to be similar to that of real-world testing data.
3. Situations in which there are many categorical variables.
4. Large number of observations in training data.
5. Number of features is smaller than the number of observations in training data.

When to NOT use XGBoost GPU Servers?

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1. Number of observations in training data are significantly smaller than the number of features.
2. Computer vision
3. Natural language processing
4. Regression tasks that involve predicting a continuous output.
5. Predict increases in targets beyond the range present in the training data.
6. Tasks involving extrapolation.

TensorFlow vs Keras: Key Differences Between Them

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1. Keras is a high-level API that can run on top of TensorFlow, CNTK, and Theano, whereas TensorFlow is a framework that offers both high and low-level APIs.
2. Keras is perfect for quick implementations, while Tensorflow is ideal for Deep learning research and complex networks.
3. Keras uses API debug tools, such as TFDBG. On the other hand, in Tensorflow, you can use Tensor board visualization tools for debugging.
4. Keras has a simple architecture that is readable and concise, while Tensorflow is not very easy to use.
5. Keras is usually used for small datasets, but TensorFlow is used for high-performance models and large datasets.
6. In Keras, community support is minimal, while in TensorFlow, it is backed by a large community of tech companies.
7. Keras is mostly used for low-performance models, whereas TensorFlow can be used for high-performance models.

Sample: # GPU-Accelerated SHAP values model.set_param({"predictor": "gpu_predictor"}) shap_values = model.predict(dtrain, pred_contribs=True) shap_interaction_values = model.predict(dtrain, pred_interactions=True)

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