update algorithm in projects.json

docker/dbmongo/files/projects.json

@@ -37,7 +37,15 @@
     },
     "category": "classification"
   },
-
+  {
+    "name": "GaussianNB",
+    "path": "sklearn.naive_bayes",
+    "categorical_encoding_strategy": "OneHotEncoder",
+    "description": "Gaussian Naive Bayes",
+    "url": "http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html",
+    "schema": {},
+    "category": "classification"
+  },
   {
     "name": "MultinomialNB",
     "path": "sklearn.naive_bayes",
@@ -281,7 +289,62 @@
     },
     "category": "classification"
   },
-
+  {
+    "name": "XGBClassifier",
+    "path": "xgboost",
+    "categorical_encoding_strategy": "OrdinalEncoder",
+    "description": "eXtreme Gradient Boosting classification",
+    "url": "https://xgboost.readthedocs.io/en/latest/tutorials/model.html",
+    "schema": {
+      "n_estimators": {
+        "description": "The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.",
+        "type": "int",
+        "default": 100,
+        "ui": {
+          "style": "radio",
+          "choices": [100, 500],
+          "grid_search": [100]
+        }
+      },
+      "learning_rate": {
+        "description": "Learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.",
+        "type": "float",
+        "default": 0.1,
+        "ui": {
+          "style": "radio",
+          "choices": [0.01, 0.1, 1]
+        }
+      },
+      "max_depth": {
+        "description": "Maximum tree depth for base learners.",
+        "type": "int",
+        "default": 3,
+        "ui": {
+          "style": "radio",
+          "choices": [1, 3, 5, 10]
+        }
+      },
+      "min_child_weight": {
+        "description": "Minimum sum of instance weight(hessian) needed in a child.",
+        "type": "int",
+        "default": 3,
+        "ui": {
+          "style": "radio",
+          "choices": [1, 3, 5, 10, 20]
+        }
+      },
+      "subsample": {
+        "description": "The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample \u003c 1.0 leads to a reduction of variance and an increase in bias.",
+        "type": "float",
+        "default": 1,
+        "ui": {
+          "style": "radio",
+          "choices": [0.5, 1]
+        }
+      }
+    },
+    "category": "classification"
+  },
   {
     "name": "KNeighborsClassifier",
     "path": "sklearn.neighbors",
@@ -585,7 +648,6 @@
     },
     "category": "classification"
   },
-
   {
     "name": "DecisionTreeRegressor",
     "path": "sklearn.tree",