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Open-EO · Dec 12, 2023 · e98dd7f · e98dd7f
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Showing 3 changed files with 4 additions and 51 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -33,7 +33,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
     - `flatten_dimensions`
     - `load_geojson`
     - `load_url`
-    - `ml_fit_class_xgboost`
     - `unflatten_dimension`
     - `vector_buffer`
     - `vector_reproject`

diff --git a/meta/subtype-schemas.json b/meta/subtype-schemas.json
@@ -232,12 +232,6 @@
                 }
             }
         },
-        "ml-model": {
-            "type": "object",
-            "subtype": "ml-model",
-            "title": "Machine Learning Model",
-            "description": "A machine learning model, accompanied with STAC metadata that implements the the STAC ml-model extension."
-        },
         "output-format": {
             "type": "string",
             "subtype": "output-format",
@@ -426,4 +420,4 @@
             "description": "Year as integer, can be any number of digits and can be negative."
         }
     }
-}
+}
diff --git a/proposals/ml_fit_class_xgboost.json b/proposals/ml_fit_class_xgboost.json
@@ -1,15 +1,15 @@
 {
     "id": "ml_fit_class_xgboost",
     "summary": "Train an XGBoost classification model",
-    "description": "Executes the fit of an XGBoost classification model based on training data.",
+    "description": "Fit an XGBoost classification model to training data. XGBoost is a high-performance, flexible, and portable distributed gradient boosting library. It implements machine learning algorithms within the Gradient Boosting framework, featuring parallel tree boosting for efficiency",
     "categories": [
         "machine learning"
     ],
     "experimental": true,
     "parameters": [
         {
             "name": "predictors",
-            "description": "The predictors for the XGBoost classification model as a vector data cube. Aggregated to the features (vectors) of the target input variable.",
+            "description": "The predictors for the XGBoost classification model as a vector data cube. They are the independent variables that the XGBoost algorithm analyses to learn patterns and relationships within the data.",
             "schema": {
                 "type": "object",
                 "subtype": "datacube",
@@ -25,7 +25,7 @@
         },
         {
             "name": "target",
-            "description": "Labeled data for XGBoost classification, aligning with predictor values based on a shared geometry dimension. This ensures a clear connection between predictor rows and labels, allowing the model to associate specific predictor values with rows during training.",
+            "description": "Labeled data for XGBoost classification, aligning with predictor values based on a shared geometry dimension. This ensures a clear connection between predictor rows and labels.",
             "schema": {
                 "type": "object",
                 "subtype": "datacube",
@@ -84,46 +84,6 @@
                 "minimum": 0
             }
         },
-        {
-            "name": "max_delta_step",
-            "description": "Maximum delta step we allow each tree's weight estimation to be.",
-            "optional": true,
-            "default": 1,
-            "schema": {
-                "type": "number",
-                "minimum": 0
-            }
-        },
-        {
-            "name": "nfold",
-            "description": "Number of folds for cross-validation.",
-            "optional": true,
-            "default": 5,
-            "schema": {
-                "type": "integer",
-                "minimum": 2
-            }
-        },
-        {
-            "name": "nrounds",
-            "description": "Number of boosting rounds.",
-            "optional": true,
-            "default": 100,
-            "schema": {
-                "type": "integer",
-                "minimum": 1
-            }
-        },
-        {
-            "name": "early_stopping_rounds",
-            "description": "Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training.",
-            "optional": true,
-            "default": 20,
-            "schema": {
-                "type": "integer",
-                "minimum": 1
-            }
-        },
         {
             "name": "seed",
             "description": "A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.",