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python - How to increase performance of LightGBM for
Oct 07, 2021 · Hyper-parameter tuning normally boosts your performance by 1-5%. Therefore, I would recommend you to check your features. Maybe you can generate new ones from the current feature space or create cross-features, discard collinear features etc.
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Improve the Performance of XGBoost and LightGBM Inference
To compare performance of stock XGBoost and LightGBM with daal4py acceleration, the prediction times for both original and converted models were measured. Figure 1 shows that daal4py is up to 36x faster than XGBoost (24x faster on average) and up to 15.5x faster than LightGBM …
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Improve lightGBM performance on clusters with multiple
Apr 20, 2018 · Improve lightGBM performance on clusters with multiple cores per executor #292. imatiach-msft opened this issue Apr 20, 2018 · 36 comments Comments. Copy link Contributor imatiach-msft commented Apr 20, 2018.
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Understanding LightGBM Parameters (and How to Tune …
Regularization. Use small max_bin. Use small num_leaves. Use min_data_in_leaf and min_sum_hessian_in_leaf. Use bagging by set bagging_fraction and bagging_freq. Use feature sub-sampling by set feature_fraction. Use bigger training data. Try lambda_l1, lambda_l2 and min_gain_to_split for ...
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"[Project]" How to increase performance of LightGBM for
to be used with LGBMRanker, Initially my NDCG scores were quite high, however by running the predicted ranking against a correct validation set from the teacher the NDCG score drops considerably (0.78 to 0.5). I tweaked my parameters to this to reduce overfitting and I've also run a series of F-score tests, mutual information tests and random ...
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How to increase performance of LightGBM for ranking
How to increase performance of LightGBM for ranking Can anyone share some advice on how to improve NDCG score for a learning to rank project using LightGBM. Currently working on a school project that requires a learning to rank functionality to rank documents per query, I have trained my model with the following parameters:
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How to Tune the Hyperparameters for Better Performance
Dec 10, 2020 · Tune hyperparameters to improve the model performance. We will add new hyperparameters as well as adjusting the existing ones in order to reduce overfitting. The first one is the min_data_in_leaf parameter. Min_data_in_leaf: The least number of data points a leaf must have.
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How many features can LightGBM select before training?
For example, if you set it to 0.6, LightGBM will select 60% of features before training each tree. There are two usage for this feature: This parameter control max depth of each trained tree and will have impact on:
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How to improve the performance of XGBoost and LightGBM?
This performance benefit is now available in XGBoost and LightGBM. All gradient boosting implementations perform similar operations, and therefore have similar data storage. In theory, this facilitates the conversion of trained models from one machine learning framework to another.
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What are the advantages of using LightGBM algorithm?
One of the advantages of using lightgbm is that it can handle categorical features very well. Yes, this algorithm is very powerful but you have to be careful about how to use its parameters. lightgbm uses a special integer-encoded method (proposed by Fisher) for handling categorical features
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How to tune LightGBM parameters before or after tuning?
Analysis of results hyperparameter Before tuning After tuning learning_rate 0.4 0.094 max_depth 15 10 num_leaves 32 12 feature_fraction 0.8 0.1 3 more rows ...
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