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l2g_xgboost_no_cross_val_777

Tabular Classification
Scikit-learn
skops
Model card Files
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test model

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Files changed (6) hide show
  1. .gitattributes +1 -0
  2. README.md +217 -0
  3. classifier_no_cross_val.skops +3 -0
  4. config.json +196 -0
  5. test.parquet +3 -0
  6. train.parquet +3 -0
.gitattributes CHANGED
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+ classifier_no_cross_val.skops filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ model_format: skops
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+ model_file: classifier_no_cross_val.skops
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+ widget:
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+ ---
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+
133
+ # Model description
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+
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+ The locus-to-gene (L2G) model derives features to prioritise likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are:
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+
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+ - Distance: (from credible set variants to gene)
138
+ - Molecular QTL Colocalization
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+ - Variant Pathogenicity: (from VEP)
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+
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+ More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/
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+
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+
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+ ## Intended uses & limitations
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+
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+ [More Information Needed]
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+
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+ ## Training Procedure
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+
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+ Gradient Boosting Classifier
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+
152
+ ### Hyperparameters
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+
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+ <details>
155
+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
158
+ |-------------------------|-----------------|
159
+ | objective | binary:logistic |
160
+ | base_score | |
161
+ | booster | |
162
+ | callbacks | |
163
+ | colsample_bylevel | |
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+ | colsample_bynode | |
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+ | colsample_bytree | 0.8 |
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+ | device | |
167
+ | early_stopping_rounds | |
168
+ | enable_categorical | False |
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+ | eval_metric | aucpr |
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+ | feature_types | |
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+ | feature_weights | |
172
+ | gamma | |
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+ | grow_policy | |
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+ | importance_type | |
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+ | interaction_constraints | |
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+ | learning_rate | |
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+ | max_bin | |
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+ | max_cat_threshold | |
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+ | max_cat_to_onehot | |
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+ | max_delta_step | |
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+ | max_depth | 5 |
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+ | max_leaves | |
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+ | min_child_weight | 10 |
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+ | missing | nan |
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+ | monotone_constraints | |
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+ | multi_strategy | |
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+ | n_estimators | |
188
+ | n_jobs | |
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+ | num_parallel_tree | |
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+ | random_state | 777 |
191
+ | reg_alpha | 1 |
192
+ | reg_lambda | 1.0 |
193
+ | sampling_method | |
194
+ | scale_pos_weight | 0.8 |
195
+ | subsample | 0.8 |
196
+ | tree_method | |
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+ | validate_parameters | |
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+ | verbosity | |
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+ | eta | 0.05 |
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+
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+ </details>
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+
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+ # How to Get Started with the Model
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+
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+ To use the model, you can load it using the `LocusToGeneModel.load_from_hub` method. This will return a `LocusToGeneModel` object that can be used to make predictions on a feature matrix.
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+ The model can then be used to make predictions using the `predict` method.
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+
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+ More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
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+
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+
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+ # Citation
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+
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+ https://doi.org/10.1038/s41588-021-00945-5
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+
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+ # License
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+
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+ MIT
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