diff --git a/examples/Home Credit - Credit Risk Model Stability.ipynb b/examples/Home Credit - Credit Risk Model Stability.ipynb index 1c061ae..e71f97b 100644 --- a/examples/Home Credit - Credit Risk Model Stability.ipynb +++ b/examples/Home Credit - Credit Risk Model Stability.ipynb @@ -1487,7 +1487,455 @@ "cell_type": "code", "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+                            "              colsample_bylevel=None, colsample_bynode=None,\n",
+                            "              colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
+                            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+                            "              gamma=None, grow_policy=None, importance_type=None,\n",
+                            "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
+                            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+                            "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
+                            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+                            "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+                            "              num_parallel_tree=None, random_state=42, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=0.05, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=4, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=100, n_jobs=None,\n", + " num_parallel_tree=None, random_state=42, ...)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import xgboost as xgb\n", "\n", @@ -1506,9 +1954,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0339 , 0.03305925, 0.03192892, ..., 0.02614957, 0.02793066,\n", + " 0.02849679], dtype=float32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Predict\n", "xgboost.predict_proba(X_train_transformed)[:, 1]" @@ -1523,18 +1983,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: lightgbm in /Users/claypot/miniconda3/envs/ibisml-dev/lib/python3.12/site-packages (4.3.0)\n", + "Requirement already satisfied: numpy in /Users/claypot/miniconda3/envs/ibisml-dev/lib/python3.12/site-packages (from lightgbm) (1.26.4)\n", + "Requirement already satisfied: scipy in /Users/claypot/miniconda3/envs/ibisml-dev/lib/python3.12/site-packages (from lightgbm) (1.13.1)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "pip install lightgbm" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p = {'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'num_leaves': 31, 'random_state': 5, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 0.8, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'objective': 'binary', 'metric': ['binary'], 'num_threads': 8}\n", + "early_stopping_round\n" + ] + } + ], "source": [ "from lightgbm import LGBMClassifier\n", "\n",