Repository: deepchecks/deepchecks

Issue:
[DOCS] Single Feature Contribution Train Test example improvement
Please follow the guidelines from this issue: #543 to improve it


Discussion:

Resolving patch:
diff --git a/deepchecks/base/display_suite.py b/deepchecks/base/display_suite.py
--- a/deepchecks/base/display_suite.py
+++ b/deepchecks/base/display_suite.py
@@ -306,7 +306,7 @@ def display_suite_result(suite_name: str, results: List[Union[CheckResult, Check
             Each check may contain conditions (which will result in pass / fail / warning, represented by {icons})
             as well as other outputs such as plots or tables.<br>
             Suites, checks and conditions can all be modified (see the
-            <a href={suite_creation_example_link}>Create a Custom Suite</a> tutorial).
+            <a href={suite_creation_example_link} target="_blank">Create a Custom Suite</a> tutorial).
         </p>
         """
 
diff --git a/deepchecks/checks/methodology/single_feature_contribution.py b/deepchecks/checks/methodology/single_feature_contribution.py
--- a/deepchecks/checks/methodology/single_feature_contribution.py
+++ b/deepchecks/checks/methodology/single_feature_contribution.py
@@ -23,8 +23,9 @@
 
 FC = t.TypeVar('FC', bound='SingleFeatureContribution')
 
-pps_url = 'https://tinyurl.com/2p8rd3jy'
-pps_html_url = f'<a href={pps_url}>Predictive Power Score</a>'
+pps_url = 'https://docs.deepchecks.com/en/stable/examples/checks/methodology/single_feature_contribution_train_test' \
+          '.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link'
+pps_html_url = f'<a href={pps_url} target="_blank">Predictive Power Score</a>'
 
 
 class SingleFeatureContribution(SingleDatasetBaseCheck):


Test patch:
diff --git a/deepchecks/checks/methodology/single_feature_contribution_train_test.py b/deepchecks/checks/methodology/single_feature_contribution_train_test.py
--- a/deepchecks/checks/methodology/single_feature_contribution_train_test.py
+++ b/deepchecks/checks/methodology/single_feature_contribution_train_test.py
@@ -23,8 +23,9 @@
 
 FC = t.TypeVar('FC', bound='SingleFeatureContributionTrainTest')
 
-pps_url = 'https://tinyurl.com/2p8rd3jy'
-pps_html_url = f'<a href={pps_url}>Predictive Power Score</a>'
+pps_url = 'https://docs.deepchecks.com/en/stable/examples/checks/methodology/single_feature_contribution_train_test' \
+          '.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link'
+pps_html_url = f'<a href={pps_url} target="_blank">Predictive Power Score</a>'
 
 
 class SingleFeatureContributionTrainTest(TrainTestBaseCheck):
diff --git a/docs/source/examples/checks/methodology/single_feature_contribution_train_test.ipynb b/docs/source/examples/checks/methodology/single_feature_contribution_train_test.ipynb
--- a/docs/source/examples/checks/methodology/single_feature_contribution_train_test.ipynb
+++ b/docs/source/examples/checks/methodology/single_feature_contribution_train_test.ipynb
@@ -5,123 +5,122 @@
    "id": "21c5d5bc",
    "metadata": {},
    "source": [
-    "# Single Feature Contribution Train Test "
+    "# Single Feature Contribution Train Test \n",
+    "\n",
+    "This notebook provides an overview for using and understanding the \"Single Feature Contribution Train Test\" check.\n",
+    "\n",
+    "**Structure:**\n",
+    "\n",
+    "- [What is the purpose of the check?](#What-is-the-purpose-of-the-check?)\n",
+    "- [Generate data](#Generate-data)\n",
+    "- [Run the check](#Run-the-check)\n",
+    "- [Define a condition](#Define-a-condition)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0fdeac8e-ffd2-4509-8a70-fcbda9cdb68b",
+   "id": "5864599b-530b-4304-8729-34fb171257d7",
    "metadata": {},
    "source": [
-    "## Imports"
+    "## What is the purpose of the check?\n",
+    "\n",
+    "The check estimates for every feature its ability to predict the label by itself. This check can help find:\n",
+    "\n",
+    "* A potential leakage (between the label and a feature) in both datasets - e.g. due to incorrect sampling during data collection. This is a critical problem, that will likely stay hidden without this check (as it won't pop up when comparing model performance on train and test).\n",
+    "* A strong drift between the the feature-label relation in both datasets, possibly originating from a leakage in one of the datasets - e.g. a leakage that exists in the training data, but not necessarily in a \"fresh\" dataset, that may have been built differently.\n",
+    "\n",
+    "The check is based on calculating the predictive power score (PPS) of each feature. For more details you can read here [how the PPS is calculated.](#How-is-the-Predictive-Power-Score-(PPS)-calculated?)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "592d6a95-06b0-4397-bf5e-5aebd6dc58f3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-01-15T00:57:38.163641Z",
-     "iopub.status.busy": "2022-01-15T00:57:38.162940Z",
-     "iopub.status.idle": "2022-01-15T00:57:40.159431Z",
-     "shell.execute_reply": "2022-01-15T00:57:40.158760Z"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "33ba1a46-14dc-4799-a8f5-d008880e560e",
+   "metadata": {},
+   "source": [
+    "### What is a problematic result?\n",
+    "1. Features with a high predictive score - can indicate that there is a leakage between the label and the feature, meaning that the feature holds information that is somewhat based on the label to begin with.\\\n",
+    "For example: a bank uses their loans database to create a model of whether a customer will be able to return a loan. One of the features they extract is \"number of late payments\". It is clear this feature will have a very strong prediction power on the customer's ability to return his loan, but this feature is based on data the bank knows only after the loan is given, so it won't be available during the time of the prediction, and is a type of leakage.\n",
+    "\n",
+    "2. A high difference between the PPS scores of a certain feature in the train and in the test datasets - this is an indication for a drift between the relation of the feature and the label and a possible leakage in one of the datasets.\\\n",
+    "For example: a coffee shop chain trained a model to predict the number of coffee cups ordered in a store, and the model was trained on data from a specific state, and tested on data from all states. Running the Single Feature Contribution check on this split found that there was a high difference in the PPS score of the feature \"time_in_day\" - it had a much higher predictive power on the training data than on the test data. Investigating this topic led to detection of the problem - the time in day was saved in UTC time for all states, which made the feature much less indicative for the test data as it had data from several time zones (and much more coffee cups are ordered in during the morning/noon than during the evening/night time). This was fixed by changing the feature to be the time relative to the local time zone, thus fixing its predictive power and improving the model's overall performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d2250ba-b193-48cb-904b-474e70fa95ef",
+   "metadata": {},
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
+    "### How is the Predictive Power Score (PPS) calculated?\n",
     "\n",
-    "from deepchecks.checks.methodology import *\n",
-    "from deepchecks.base import Dataset\n",
+    "The features' predictive score results in a numeric score between 0 (feature has no predictive power) and 1 (feature can fully predict the label alone).\\\n",
     "\n",
-    "np.random.seed(42)"
+    "The process of calculating the PPS is the following:\n",
+    "1. Extract from the data only the label and the feature being tested\n",
+    "2. Drop samples with missing values\n",
+    "3. Keep 5000 (this is configurable parameter) samples from the data\n",
+    "4. Preprocess categorical columns. For the label using `sklearn.LabelEncoder` and for the feature using `sklearn.OneHotEncoder`\n",
+    "5. Partition the data with 4-fold cross-validation\n",
+    "6. Train decision tree\n",
+    "7. Compare the trained model's performance with naive model's performance as follows:\\\n",
+    "Regression: The naive model always predicts the median of the label column, The metric being used is MAE and the PPS calculation is: $1 - \\frac{\\text{MAE model}}{\\text{MAE naive}}$\\\n",
+    "Classification: The naive model always predicts the most common class of the label column, The metric being used is F1 and the PPS calculation is: $\\frac{\\text{F1 model} - \\text{F1 naive}}{1 - \\text{F1 naive}}$\n",
+    "\n",
+    "*Note: all the PPS parameters can be changed by passing to the check the parameter `ppscore_params`*\n",
+    "\n",
+    "For further information about PPS you can visit the <a href=\"https://github.com/8080labs/ppscore\" target=\"_blank\">ppscore github</a> or the following blog post: <a href=\"https://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598\" target=\"_blank\">RIP correlation. Introducing the Predictive Power Score</a>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7c64c053-69ea-4579-85f7-a27c8df9b2b7",
+   "id": "0fdeac8e-ffd2-4509-8a70-fcbda9cdb68b",
    "metadata": {},
    "source": [
-    "## Generating data:"
+    "## Generate data\n",
+    "\n",
+    "We'll add to a given dataset a direct relation between two features and the label, in order to see the Single Feature Contribution Train Test check in action."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "82156e41-2ec0-4374-8716-f9a2ab09fbd8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-01-15T00:57:40.184230Z",
-     "iopub.status.busy": "2022-01-15T00:57:40.183618Z",
-     "iopub.status.idle": "2022-01-15T00:57:40.186916Z",
-     "shell.execute_reply": "2022-01-15T00:57:40.187424Z"
-    }
-   },
+   "execution_count": 1,
+   "id": "9242c05f-d312-4c00-977e-54032d49852e",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "def generate_data():\n",
-    "    df = pd.DataFrame(np.random.randn(100, 3), columns=['x1', 'x2', 'x3'])\n",
-    "    df['x4'] = df['x1'] * 0.5 + df['x2']\n",
-    "    df['label'] = df['x2'] + 0.1 * df['x1']\n",
-    "    df['x5'] = df['label'].apply(lambda x: 'v1' if x < 0 else 'v2')\n",
-    "    return df\n",
+    "from deepchecks.datasets.classification.phishing import load_data\n",
     "\n",
-    "df_train = generate_data()\n",
-    "ds_train = Dataset(df_train, label='label')\n",
+    "def relate_column_to_label(dataset, column, label_power):\n",
+    "    col_data = dataset.data[column]\n",
+    "    dataset.data[column] = col_data + (dataset.label_col * col_data.mean() * label_power)\n",
+    "    \n",
+    "train_dataset, test_dataset = load_data()\n",
     "\n",
-    "df_test = generate_data()\n",
-    "df_test['x2'] = df_test['x2'] + 0.5 * df_test['x1']\n",
-    "df_test['x3'] = 0.5 * df_test['x3'] + 0.5 * df_test['label']\n",
-    "df_test['x5'][:15] = 'v1'\n",
-    "ds_test = Dataset(df_test, label='label')"
+    "# Transforming 2 features in the dataset given to add correlation to the label \n",
+    "relate_column_to_label(train_dataset, 'numDigits', 10)\n",
+    "relate_column_to_label(train_dataset, 'numLinks', 10)\n",
+    "relate_column_to_label(test_dataset, 'numDigits', 0.1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5ff59076-2895-4b56-84f2-d555deb2660b",
+   "id": "8b5defd1-376a-4229-88c8-1e273d124d9c",
    "metadata": {},
    "source": [
-    "## Using the SingleFeatureContributionTrainTest check class:"
+    "## Run the check"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "88357833-807f-4d0e-86a3-9459f80d465d",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-01-15T00:57:40.203052Z",
-     "iopub.status.busy": "2022-01-15T00:57:40.200683Z",
-     "iopub.status.idle": "2022-01-15T00:57:40.497243Z",
-     "shell.execute_reply": "2022-01-15T00:57:40.488295Z"
-    }
+    "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h4>Single Feature Contribution Train-Test</h4>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label.</p>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<h5>Additional Outputs</h5>"
+       "<h4>Single Feature Contribution Train-Test</h4><p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label.</p><h5>Additional Outputs</h5>"
       ]
      },
      "metadata": {},
@@ -226,27 +225,27 @@
          },
          "name": "Train",
          "text": [
-          0.84,
-          0.42,
-          0.0,
-          0.53,
-          0.0
+          0.89,
+          0.95,
+          0.24,
+          0,
+          0
          ],
          "textposition": "outside",
          "type": "bar",
          "x": [
-          "x2",
-          "x5",
-          "x3",
-          "x4",
-          "x1"
+          "numLinks",
+          "numDigits",
+          "urlLength",
+          "urlIsLive",
+          "sscr"
          ],
          "y": [
-          0.841523366731384,
-          0.42269099121738773,
-          0.0,
-          0.5334285394903233,
-          0.0
+          0.8851351351351351,
+          0.9527027027027027,
+          0.23646714795497537,
+          0,
+          0
          ]
         },
         {
@@ -255,27 +254,27 @@
          },
          "name": "Test",
          "text": [
-          0.54,
-          0.36,
-          0.05,
-          0.54,
-          0.0
+          0,
+          0.84,
+          0.27,
+          0,
+          0
          ],
          "textposition": "outside",
          "type": "bar",
          "x": [
-          "x2",
-          "x5",
-          "x3",
-          "x4",
-          "x1"
+          "numLinks",
+          "numDigits",
+          "urlLength",
+          "urlIsLive",
+          "sscr"
          ],
          "y": [
-          0.536060975234053,
-          0.3582337021282237,
-          0.04995065106093688,
-          0.536060975234053,
-          0.0
+          0,
+          0.8367346292159752,
+          0.2723349191922806,
+          0,
+          0
          ]
         },
         {
@@ -290,18 +289,18 @@
          "name": "Train-Test Difference (abs)",
          "type": "scatter",
          "x": [
-          "x2",
-          "x5",
-          "x3",
-          "x4",
-          "x1"
+          "numLinks",
+          "numDigits",
+          "urlLength",
+          "urlIsLive",
+          "sscr"
          ],
          "y": [
-          0.3054623914973309,
-          0.06445728908916404,
-          0.04995065106093688,
-          0.002632435743729733,
-          0.0
+          0.8851351351351351,
+          0.11596807348672755,
+          0.035867771237305224,
+          0,
+          0
          ]
         }
        ],
@@ -309,8 +308,8 @@
         "barmode": "group",
         "height": 500,
         "legend": {
-         "x": 1.0,
-         "y": 1.0
+         "x": 1,
+         "y": 1
         },
         "template": {
          "data": {
@@ -388,7 +387,7 @@
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             "colorscale": [
              [
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+              0,
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@@ -424,7 +423,7 @@
               "#fdca26"
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@@ -448,7 +447,7 @@
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@@ -484,7 +483,7 @@
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@@ -499,7 +498,7 @@
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@@ -535,7 +534,7 @@
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@@ -613,7 +612,7 @@
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@@ -922,7 +921,7 @@
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@@ -958,13 +957,13 @@
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@@ -1134,9 +1133,15 @@
         },
         "width": 800,
         "xaxis": {
+         "autorange": true,
+         "range": [
+          -0.5,
+          4.5
+         ],
          "title": {
           "text": "Column"
-         }
+         },
+         "type": "category"
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         "yaxis": {
          "range": [
@@ -1145,14 +1150,16 @@
          ],
          "title": {
           "text": "Predictive Power Score (PPS)"
-         }
+         },
+         "type": "linear"
         }
        }
       },
+      "image/png": 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        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1183,88 +1190,192 @@
     {
      "data": {
       "text/html": [
-       "The PPS (<a href=https://tinyurl.com/2p8rd3jy>Predictive Power Score</a>) is used to estimate the ability of a feature to predict the label by itself."
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<u>In the graph above</u>, we should suspect we have problems in our data if:"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "1. <b>Train dataset PPS values are high</b>:"
+       "<div>The PPS (<a href=https://tinyurl.com/2p8rd3jy>Predictive Power Score</a>) is used to estimate the ability of a feature to predict the label by itself.</div><div><u>In the graph above</u>, we should suspect we have problems in our data if:</div><div>1. <b>Train dataset PPS values are high</b>:</div><div>Can indicate that this feature's success in predicting the label is actually due to data leakage, </div><div>   meaning that the feature holds information that is based on the label to begin with.</div><div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div><div>   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test </div><div>   can be explained by leakage in train that is not relevant to a new dataset.</div><div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div><div>   An anomalous value, could indicate  drift in test dataset that caused a coincidental correlation to the target label.</div>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "Can indicate that this feature's success in predicting the label is actually due to data leakage, "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "   meaning that the feature holds information that is based on the label to begin with."
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "2. <b>Large difference between train and test PPS</b> (train PPS is larger):"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "   An even more powerful indication of data leakage, as a feature that was powerful in train but not in test "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "   can be explained by leakage in train that is not relevant to a new dataset."
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "from deepchecks.checks.methodology import SingleFeatureContributionTrainTest\n",
+    "\n",
+    "result = SingleFeatureContributionTrainTest().run(train_dataset=train_dataset, test_dataset=test_dataset)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e284d6d0-9aa9-4327-9b36-e91b1d6fb53b",
+   "metadata": {},
+   "source": [
+    "### Observe the check's output\n",
+    "\n",
+    "The check shows the top features with the highest PPS difference in the datasets, and elaborates how to interpret the results.\\\n",
+    "By default only the top 5 features are displayed, it can be changed by using the parameter `n_show_top` of the check.\n",
+    "\n",
+    "In addition to the graphic output, the check also returns a value which includes all of the information that is needed for defining the conditions for validation.\\\n",
+    "The value is a dictionary of:\n",
+    "- train - for train dataset for each column the numeric PPS score (0 to 1)\n",
+    "- test - for test dataset for each column the numeric PPS score (0 to 1)\n",
+    "- train-test difference - for each column the difference between the datasets scores, as `train - test`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4f079c66-1373-4a20-8654-d92fb4ecc513",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "3. <b>Large difference between test and train PPS</b> (test PPS is larger):"
+      "text/plain": [
+       "{'train': {'numDigits': 0.9527027027027027,\n",
+       "  'numLinks': 0.8851351351351351,\n",
+       "  'urlLength': 0.23646714795497537,\n",
+       "  'month': 0.0,\n",
+       "  'ext': 0.0,\n",
+       "  'numParams': 0.0,\n",
+       "  'num_%20': 0.0,\n",
+       "  'num_@': 0.0,\n",
+       "  'entropy': 0.0,\n",
+       "  'has_ip': 0.0,\n",
+       "  'hasHttp': 0.0,\n",
+       "  'hasHttps': 0.0,\n",
+       "  'urlIsLive': 0.0,\n",
+       "  'dsr': 0.0,\n",
+       "  'dse': 0.0,\n",
+       "  'bodyLength': 0.0,\n",
+       "  'numTitles': 0.0,\n",
+       "  'numImages': 0.0,\n",
+       "  'specialChars': 0.0,\n",
+       "  'scriptLength': 0.0,\n",
+       "  'sbr': 0.0,\n",
+       "  'bscr': 0.0,\n",
+       "  'sscr': 0.0},\n",
+       " 'test': {'numDigits': 0.8367346292159752,\n",
+       "  'urlLength': 0.2723349191922806,\n",
+       "  'month': 0.0,\n",
+       "  'ext': 0.0,\n",
+       "  'numParams': 0.0,\n",
+       "  'num_%20': 0.0,\n",
+       "  'num_@': 0.0,\n",
+       "  'entropy': 0.0,\n",
+       "  'has_ip': 0.0,\n",
+       "  'hasHttp': 0.0,\n",
+       "  'hasHttps': 0.0,\n",
+       "  'urlIsLive': 0.0,\n",
+       "  'dsr': 0.0,\n",
+       "  'dse': 0.0,\n",
+       "  'bodyLength': 0.0,\n",
+       "  'numTitles': 0.0,\n",
+       "  'numImages': 0.0,\n",
+       "  'numLinks': 0.0,\n",
+       "  'specialChars': 0.0,\n",
+       "  'scriptLength': 0.0,\n",
+       "  'sbr': 0.0,\n",
+       "  'bscr': 0.0,\n",
+       "  'sscr': 0.0},\n",
+       " 'train-test difference': {'bodyLength': 0.0,\n",
+       "  'bscr': 0.0,\n",
+       "  'dse': 0.0,\n",
+       "  'dsr': 0.0,\n",
+       "  'entropy': 0.0,\n",
+       "  'ext': 0.0,\n",
+       "  'hasHttp': 0.0,\n",
+       "  'hasHttps': 0.0,\n",
+       "  'has_ip': 0.0,\n",
+       "  'month': 0.0,\n",
+       "  'numDigits': 0.11596807348672755,\n",
+       "  'numImages': 0.0,\n",
+       "  'numLinks': 0.8851351351351351,\n",
+       "  'numParams': 0.0,\n",
+       "  'numTitles': 0.0,\n",
+       "  'num_%20': 0.0,\n",
+       "  'num_@': 0.0,\n",
+       "  'sbr': 0.0,\n",
+       "  'scriptLength': 0.0,\n",
+       "  'specialChars': 0.0,\n",
+       "  'sscr': 0.0,\n",
+       "  'urlIsLive': 0.0,\n",
+       "  'urlLength': -0.035867771237305224}}"
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49ad3bb9-2ce9-4318-8459-5b52bc7bd785",
+   "metadata": {},
+   "source": [
+    "## Define a condition\n",
+    "\n",
+    "We can define on our check a condition that will validate that our pps scores aren't too high. The check has 2 possible built-in conditions:\\\n",
+    "`add_condition_feature_pps_difference_not_greater_than` - Validate that the difference in the PPS between train and test is not larger than defined amount (default 0.2)\\\n",
+    "`add_condition_feature_pps_in_train_not_greater_than` - Validate that the PPS scores on train dataset are not exceeding a defined amount (default 0.7)\n",
+    "\n",
+    "Let's add the conditions, and re-run the check:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3baa5b05-4ea1-4e32-909a-2d6c692bf7fc",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/html": [
-       "   An anomalous value, could indicate  drift in test dataset that caused a coincidental correlation to the target label."
+       "<h4>Single Feature Contribution Train-Test</h4><p>    Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label.</p><h5>Conditions Summary</h5><style type=\"text/css\">\n",
+       "#T_d0a3f_ table {\n",
+       "  text-align: left;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "#T_d0a3f_ thead {\n",
+       "  text-align: left;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "#T_d0a3f_ tbody {\n",
+       "  text-align: left;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "#T_d0a3f_ th {\n",
+       "  text-align: left;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "#T_d0a3f_ td {\n",
+       "  text-align: left;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_d0a3f_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"col_heading level0 col0\" >Status</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Condition</th>\n",
+       "      <th class=\"col_heading level0 col2\" >More Info</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td id=\"T_d0a3f_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">✖</div></td>\n",
+       "      <td id=\"T_d0a3f_row0_col1\" class=\"data row0 col1\" >Train-Test features' <a href=https://tinyurl.com/2p8rd3jy>Predictive Power Score</a> (PPS) difference is not greater than 0.2</td>\n",
+       "      <td id=\"T_d0a3f_row0_col2\" class=\"data row0 col2\" >Features with PPS difference above threshold: {'numLinks': '0.89'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td id=\"T_d0a3f_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">✖</div></td>\n",
+       "      <td id=\"T_d0a3f_row1_col1\" class=\"data row1 col1\" >Train features' <a href=https://tinyurl.com/2p8rd3jy>Predictive Power Score</a> (PPS) is not greater than 0.7</td>\n",
+       "      <td id=\"T_d0a3f_row1_col2\" class=\"data row1 col2\" >Features in train dataset with PPS above threshold: {'numDigits': '0.95', 'numLinks': '0.89'}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ]
      },
      "metadata": {},
@@ -1272,7 +1383,17 @@
     }
    ],
    "source": [
-    "SingleFeatureContributionTrainTest().run(train_dataset=ds_train, test_dataset=ds_test)"
+    "check = SingleFeatureContributionTrainTest().add_condition_feature_pps_difference_not_greater_than().add_condition_feature_pps_in_train_not_greater_than()\n",
+    "result = check.run(train_dataset=train_dataset, test_dataset=test_dataset)\n",
+    "result.show(show_additional_outputs=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a945503-345e-4ae3-8d31-340e3a6ff9b6",
+   "metadata": {},
+   "source": [
+    "We see that the conditions have caught the changes we have introduced to the datasets, and alerts us that there is a possible problem with the given features."
    ]
   }
  ],
@@ -1292,7 +1413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/tests/checks/methodology/single_feature_contribution_test.py b/tests/checks/methodology/single_feature_contribution_test.py
--- a/tests/checks/methodology/single_feature_contribution_test.py
+++ b/tests/checks/methodology/single_feature_contribution_test.py
@@ -15,13 +15,11 @@
 
 from deepchecks import Dataset
 from deepchecks.checks.methodology import SingleFeatureContribution, SingleFeatureContributionTrainTest
+from deepchecks.checks.methodology.single_feature_contribution import pps_html_url
 from deepchecks.errors import DeepchecksValueError, DatasetValidationError
 
 from tests.checks.utils import equal_condition_result
 
-pps_url = 'https://tinyurl.com/2p8rd3jy'
-pps_html_url = f'<a href={pps_url}>Predictive Power Score</a>'
-
 
 def util_generate_dataframe_and_expected():
     np.random.seed(42)