update links to main branch

Author: paulbkoch

README.md

@@ -23,7 +23,7 @@ Interpretability is essential for:
 - Regulatory compliance - Does my model satisfy legal requirements?
 - High-risk applications - Healthcare, finance, judicial, ...
 
-![](https://github.com/interpretml/interpretml.github.io/blob/master/interpret-highlight.gif)
+![](https://github.com/interpretml/interpretml.github.io/blob/main/interpret-highlight.gif)
 
 # Installation
 

docs/interpret/deployment-guide.ipynb

@@ -73,8 +73,8 @@
     "pip install interpret-core[debug,notebook,plotly,lime,sensitivity,shap,linear,skoperules,treeinterpreter,aplr,dash,testing]\n",
     "```\n",
     "\n",
-    "- `interpret` utilizing `interpret-core` tags found [here](https://github.com/interpretml/interpret/blob/develop/python/interpret/setup.py).\n",
-    "- `interpret-core` extra tags found [here](https://github.com/interpretml/interpret/blob/develop/python/interpret-core/setup.py)."
+    "- `interpret` utilizing `interpret-core` tags found [here](https://github.com/interpretml/interpret/blob/main/python/interpret/setup.py).\n",
+    "- `interpret-core` extra tags found [here](https://github.com/interpretml/interpret/blob/main/python/interpret-core/setup.py)."
    ]
   }
  ],

docs/interpret/faq.ipynb

@@ -266,7 +266,7 @@
     "\n",
     "<h2>I have a great idea, how can I contribute?</h2>\n",
     "\n",
-    "If you have code you'd like to commit, make sure you read the [contributing guidelines](https://github.com/interpretml/interpret/blob/master/CONTRIBUTING.md), and send us a pull request.\n",
+    "If you have code you'd like to commit, make sure you read the [contributing guidelines](https://github.com/interpretml/interpret/blob/main/CONTRIBUTING.md), and send us a pull request.\n",
     "\n",
     "If you'd like to request a new feature or discuss any ideas, just raise a [GitHub issue](https://github.com/interpretml/interpret/issues)."
    ]

docs/interpret/python/examples/custom-interactions.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "In this notebook we will disable the automatic interaction detection built into the interpret API and instead detect the interactions ourselves and then incorporate them into the EBM. We will also detect and use 3-way interactions, which are typically not needed, but can sometimes be useful.\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/differential-privacy.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "In this notebook we will build differentially private EBMs and compare them to regular EBMs.\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/explain-blackbox-classifiers.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "In this notebook we will use the interpret package to explain blackbox classifiers using SHAP, Lime, MorrisSensitivity, and PartialDependence.\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/explain-blackbox-regressors.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "In this notebook we will use the interpret package to explain blackbox regressors using SHAP, Lime, MorrisSensitivity, and PartialDependence.\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/group-importances.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "Throughout the notebook we use _term_ to denote both single features and interactions (pairs).\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/interpretable-classification.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "In this notebook we will fit classification explainable boosting machine (EBM), LogisticRegression, and ClassificationTree models. After fitting them, we will use their glassbox nature to understand their global and local explanations.\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {

docs/interpret/python/examples/interpretable-regression-synthetic.ipynb

@@ -11,9 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this demonstration notebook, we are going to create an Explainable Boosting Machine (EBM) using a specially designed synthetic dataset. Our control over the data generation process allows us to visually assess how well the EBM is able to recover the original functions that were used to create the data. To understand how the synthetic dataset was generated, you can examine the full code on GitHub. This will provide insights into the underlying functions we are trying to recover. The full dataset generation code can be found in: [**_synthetic generation code_**](https://github.com/interpretml/interpret/blob/develop/python/interpret-core/interpret/utils/_synthetic.py)\n",
+    "In this demonstration notebook, we are going to create an Explainable Boosting Machine (EBM) using a specially designed synthetic dataset. Our control over the data generation process allows us to visually assess how well the EBM is able to recover the original functions that were used to create the data. To understand how the synthetic dataset was generated, you can examine the full code on GitHub. This will provide insights into the underlying functions we are trying to recover. The full dataset generation code can be found in: [**_synthetic generation code_**](https://github.com/interpretml/interpret/blob/main/python/interpret-core/interpret/utils/_synthetic.py)\n",
     "\n",
-    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/develop/docs/interpret/python/examples) on GitHub."
+    "This notebook can be found in our [**_examples folder_**](https://github.com/interpretml/interpret/tree/main/docs/interpret/python/examples) on GitHub."
    ]
   },
   {
@@ -301,4 +301,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}