.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "user-guide/tabular/auto_tutorials/plot_quickstart_in_5_minutes.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_user-guide_tabular_auto_tutorials_plot_quickstart_in_5_minutes.py: Quickstart in 5 minutes *********************** In order to run your first Deepchecks Suite all you need to have is the data and model that you wish to validate. More specifically, you need: * Your train and test data (in Pandas DataFrames or Numpy Arrays) * (optional) A :doc:`supported model ` (including XGBoost, scikit-learn models, and many more). Required for running checks that need the model's predictions for running. To run your first suite on your data and model, you need only a few lines of code, that start here: `Define a Dataset Object <#define-a-dataset-object>`__. # If you don't have deepchecks installed yet: .. code:: python # If you don't have deepchecks installed yet: import sys !{sys.executable} -m pip install deepchecks -U --quiet #--user .. GENERATED FROM PYTHON SOURCE LINES 28-32 Load Data, Split Train-Val, and Train a Simple Model ==================================================== For the purpose of this guide we'll use the simple iris dataset and train a simple random forest model for multiclass classification: .. GENERATED FROM PYTHON SOURCE LINES 32-50 .. code-block:: default import numpy as np # General imports import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from deepchecks.tabular.datasets.classification import iris # Load Data iris_df = iris.load_data(data_format='Dataframe', as_train_test=False) label_col = 'target' df_train, df_test = train_test_split(iris_df, stratify=iris_df[label_col], random_state=0) # Train Model rf_clf = RandomForestClassifier(random_state=0) rf_clf.fit(df_train.drop(label_col, axis=1), df_train[label_col]); .. rst-class:: sphx-glr-script-out Out: .. code-block:: none RandomForestClassifier(random_state=0) .. GENERATED FROM PYTHON SOURCE LINES 51-58 Define a Dataset Object ======================= Initialize the Dataset object, stating the relevant metadata about the dataset (e.g. the name for the label column) Check out the Dataset's attributes to see which additional special columns can be declared and used (e.g. date column, index column). .. GENERATED FROM PYTHON SOURCE LINES 58-67 .. code-block:: default from deepchecks.tabular import Dataset # We explicitly state that this dataset has no categorical features, otherwise they will be automatically inferred # If the dataset has categorical features, the best practice is to pass a list with their names ds_train = Dataset(df_train, label=label_col, cat_features=[]) ds_test = Dataset(df_test, label=label_col, cat_features=[]) .. GENERATED FROM PYTHON SOURCE LINES 68-76 Run a Deepchecks Suite ====================== Run the full suite ------------------ Use the ``full_suite`` that is a collection of (most of) the prebuilt checks. Check out the :doc:`when should you use ` deepchecks guide for some more info about the existing suites and when to use them. .. GENERATED FROM PYTHON SOURCE LINES 76-81 .. code-block:: default from deepchecks.tabular.suites import full_suite suite = full_suite() .. GENERATED FROM PYTHON SOURCE LINES 82-85 .. code-block:: default suite.run(train_dataset=ds_train, test_dataset=ds_test, model=rf_clf) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Full Suite: 0%| | 0/36 [00:00

Full Suite

The suite is composed of various checks such as: Model Info, Date Train Test Leakage Overlap, Columns Info, etc...
Each check may contain conditions (which will result in pass / fail / warning / error , represented by ✓ / ✖ / ! / ⁈ ) as well as other outputs such as plots or tables.
Suites, checks and conditions can all be modified. Read more about custom suites.

Conditions Summary

Status Check Condition More Info
✖
 Performance Report Train-Test scores relative degradation is not greater than 0.1 Precision for class 1 (train=1 test=0.87) Recall for class 2 (train=1 test=0.83)
✖
 Single Feature Contribution Train-Test Train features' Predictive Power Score is not greater than 0.7 Features in train dataset with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.83'}
✓
 ROC Report - Test Dataset AUC score for all the classes is not less than 0.7
✓
 Special Characters - Test Dataset Ratio of entirely special character samples not greater than 0.1%
✓
 Special Characters - Train Dataset Ratio of entirely special character samples not greater than 0.1%
✓
 String Length Out Of Bounds - Test Dataset Ratio of outliers not greater than 0% string length outliers
✓
 String Length Out Of Bounds - Train Dataset Ratio of outliers not greater than 0% string length outliers
✓
 Data Duplicates - Test Dataset Duplicate data ratio is not greater than 0%
✓
 Data Duplicates - Train Dataset Duplicate data ratio is not greater than 0%
✓
 String Mismatch - Test Dataset No string variants
✓
 String Mismatch - Train Dataset No string variants
✓
 Mixed Data Types - Test Dataset Rare data types in column are either more than 10% or less than 1% of the data
✓
 Mixed Data Types - Train Dataset Rare data types in column are either more than 10% or less than 1% of the data
✓
 Mixed Nulls - Test Dataset Not more than 1 different null types
✓
 Mixed Nulls - Train Dataset Not more than 1 different null types
✓
 Single Value in Column - Test Dataset Does not contain only a single value
✓
 Single Value in Column - Train Dataset Does not contain only a single value
✓
 Train Test Samples Mix Percentage of test data samples that appear in train data not greater than 10%
✓
 Conflicting Labels - Train Dataset Ambiguous sample ratio is not greater than 0%
✓
 Single Feature Contribution Train-Test Train-Test features' Predictive Power Score difference is not greater than 0.2
✓
 Datasets Size Comparison Test-Train size ratio is not smaller than 0.01
✓
 String Mismatch Comparison No new variants allowed in test data
✓
 New Label Train Test Number of new label values is not greater than 0
✓
 Category Mismatch Train Test Ratio of samples with a new category is not greater than 0%
✓
 Dominant Frequency Change Change in ratio of dominant value in data is not greater than 25%
✓
 Whole Dataset Drift Drift value is not greater than 0.25
✓
 Train Test Label Drift PSI <= 0.2 and Earth Mover's Distance <= 0.1 for label drift
✓
 Train Test Drift PSI <= 0.2 and Earth Mover's Distance <= 0.1
✓
 Model Inference Time - Test Dataset Average model inference time for one sample is not greater than 0.001
✓
 Model Inference Time - Train Dataset Average model inference time for one sample is not greater than 0.001
✓
 Unused Features Number of high variance unused features is not greater than 5
✓
 Simple Model Comparison Model performance gain over simple model is not less than 10%
✓
 ROC Report - Train Dataset AUC score for all the classes is not less than 0.7
✓
 Conflicting Labels - Test Dataset Ambiguous sample ratio is not greater than 0%

Check With Conditions Output

Performance Report

Summarize given scores on a dataset and model.

Conditions Summary
Status Condition More Info
✖
 Train-Test scores relative degradation is not greater than 0.1 Precision for class 1 (train=1 test=0.87) Recall for class 2 (train=1 test=0.83)
Additional Outputs

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ROC Report - Train Dataset

Calculate the ROC curve for each class.

Conditions Summary
Status Condition More Info
✓
 AUC score for all the classes is not less than 0.7
Additional Outputs
The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined as sensitivity + specificity - 1

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ROC Report - Test Dataset

Calculate the ROC curve for each class.

Conditions Summary
Status Condition More Info
✓
 AUC score for all the classes is not less than 0.7
Additional Outputs
The marked points are the optimal threshold cut-off points. They are determined using Youden's index defined as sensitivity + specificity - 1

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Simple Model Comparison

Compare given model score to simple model score (according to given model type).

Conditions Summary
Status Condition More Info
✓
 Model performance gain over simple model is not less than 10%
Additional Outputs

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Unused Features

Detect features that are nearly unused by the model.

Conditions Summary
Status Condition More Info
✓
 Number of high variance unused features is not greater than 5
Additional Outputs
Features above the line are a sample of the most important features, while the features below the line are the unused features with highest variance, as defined by check parameters

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Model Inference Time - Train Dataset

Measure model average inference time (in seconds) per sample.

Conditions Summary
Status Condition More Info
✓
 Average model inference time for one sample is not greater than 0.001
Additional Outputs
Average model inference time for one sample (in seconds): 8.2e-05

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Model Inference Time - Test Dataset

Measure model average inference time (in seconds) per sample.

Conditions Summary
Status Condition More Info
✓
 Average model inference time for one sample is not greater than 0.001
Additional Outputs
Average model inference time for one sample (in seconds): 0.00021871

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Train Test Drift

Calculate drift between train dataset and test dataset per feature, using statistical measures.

Conditions Summary
Status Condition More Info
✓
 PSI <= 0.2 and Earth Mover's Distance <= 0.1
Additional Outputs
The Drift score is a measure for the difference between two distributions, in this check - the test and train distributions.
The check shows the drift score and distributions for the features, sorted by feature importance and showing only the top 5 features, according to feature importance.
If available, the plot titles also show the feature importance (FI) rank.

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Train Test Label Drift

Calculate label drift between train dataset and test dataset, using statistical measures.

Conditions Summary
Status Condition More Info
✓
 PSI <= 0.2 and Earth Mover's Distance <= 0.1 for label drift
Additional Outputs
The Drift score is a measure for the difference between two distributions, in this check - the test and train distributions.
The check shows the drift score and distributions for the label.

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Datasets Size Comparison

Verify test dataset size comparing it to the train dataset size.

Conditions Summary
Status Condition More Info
✓
 Test-Train size ratio is not smaller than 0.01
Additional Outputs
  Train Test
Size 112 38

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Single Feature Contribution Train-Test

Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label.

Conditions Summary
Status Condition More Info
✖
 Train features' Predictive Power Score is not greater than 0.7 Features in train dataset with PPS above threshold: {'petal width (cm)': '0.91', 'petal length (cm)': '0.83'}
✓
 Train-Test features' Predictive Power Score difference is not greater than 0.2
Additional Outputs
The Predictive Power Score (PPS) is used to estimate the ability of a feature to predict the label by itself. (Read more about Predictive Power Score)
In the graph above, we should suspect we have problems in our data if:
1. Train dataset PPS values are high:
Can indicate that this feature's success in predicting the label is actually due to data leakage,
meaning that the feature holds information that is based on the label to begin with.
2. Large difference between train and test PPS (train PPS is larger):
An even more powerful indication of data leakage, as a feature that was powerful in train but not in test
can be explained by leakage in train that is not relevant to a new dataset.
3. Large difference between test and train PPS (test PPS is larger):
An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.

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Train Test Samples Mix

Detect samples in the test data that appear also in training data.

Conditions Summary
Status Condition More Info
✓
 Percentage of test data samples that appear in train data not greater than 10%
Additional Outputs
2.63% (1 / 38) of test data samples appear in train data
  sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
Train indices: 101 Test indices: 142 5.80 2.70 5.10 1.90 2

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Check Without Conditions Output

Model Info

Summarize given model parameters.

Additional Outputs
Model Type: RandomForestClassifier
Parameter Value Default
bootstrap True True
ccp_alpha 0.00 0.00
class_weight None None
criterion gini gini
max_depth None None
max_features auto auto
max_leaf_nodes None None
max_samples None None
min_impurity_decrease 0.00 0.00
min_samples_leaf 1 1
min_samples_split 2 2
min_weight_fraction_leaf 0.00 0.00
n_estimators 100 100
n_jobs None None
oob_score False False
random_state 0 None
verbose 0 0
warm_start False False

Colored rows are parameters with non-default values


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Columns Info - Train Dataset

Return the role and logical type of each column.

Additional Outputs
* showing only the top 10 columns, you can change it using n_top_columns param
  target petal width (cm) petal length (cm) sepal length (cm) sepal width (cm)
role label numerical feature numerical feature numerical feature numerical feature

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Columns Info - Test Dataset

Return the role and logical type of each column.

Additional Outputs
* showing only the top 10 columns, you can change it using n_top_columns param
  target petal width (cm) petal length (cm) sepal length (cm) sepal width (cm)
role label numerical feature numerical feature numerical feature numerical feature

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Confusion Matrix Report - Train Dataset

Calculate the confusion matrix of the model on the given dataset.

Additional Outputs

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Confusion Matrix Report - Test Dataset

Calculate the confusion matrix of the model on the given dataset.

Additional Outputs

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Calibration Metric - Train Dataset

Calculate the calibration curve with brier score for each class.

Additional Outputs
Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.
The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score

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Calibration Metric - Test Dataset

Calculate the calibration curve with brier score for each class.

Additional Outputs
Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of a binary classifier are calibrated. It plots the true frequency of the positive label against its predicted probability, for binned predictions.
The Brier score metric may be used to assess how well a classifier is calibrated. For more info, please visit https://en.wikipedia.org/wiki/Brier_score

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Outlier Sample Detection - Train Dataset

Detects outliers in a dataset using the LoOP algorithm.

Additional Outputs
The Outlier Probability Score is calculated by the LoOP algorithm which measures the local deviation of density of a given sample with respect to its neighbors. These outlier scores are directly interpretable as a probability of an object being an outlier (see link for more information).

  Outlier Probability Score sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
41 0.89 4.50 2.30 1.30 0.30 0
106 0.72 4.90 2.50 4.50 1.70 2
56 0.57 6.30 3.30 4.70 1.60 1
114 0.56 5.80 2.80 5.10 2.40 2
22 0.56 4.60 3.60 1.00 0.20 0

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Other Checks That Weren't Displayed

Check Reason
Model Error Analysis Unable to train meaningful error model (r^2 score: 0.14)
Index Train Test Leakage There is no index defined to use. Did you pass a DataFrame instead of a Dataset?
Identifier Leakage - Test Dataset Check is irrelevant for Datasets without index or date column
Identifier Leakage - Train Dataset Check is irrelevant for Datasets without index or date column
Date Train Test Leakage Overlap There is no datetime defined to use. Did you pass a DataFrame instead of a Dataset?
Date Train Test Leakage Duplicates There is no datetime defined to use. Did you pass a DataFrame instead of a Dataset?
Outlier Sample Detection - Test Dataset NotEnoughSamplesError: There are not enough samples to run this check, found only 38 samples.
Boosting Overfit Check is relevant for Boosting models of type ('AdaBoostClassifier', 'GradientBoostingClassifier', 'LGBMClassifier', 'XGBClassifier', 'CatBoostClassifier', 'AdaBoostRegressor', 'GradientBoostingRegressor', 'LGBMRegressor', 'XGBRegressor', 'CatBoostRegressor'), but received model of type RandomForestClassifier
Regression Error Distribution - Test Dataset Check is relevant for models of type ['regression'], but received model of type 'multiclass'
Regression Error Distribution - Train Dataset Check is relevant for models of type ['regression'], but received model of type 'multiclass'
Regression Systematic Error - Test Dataset Check is relevant for models of type ['regression'], but received model of type 'multiclass'
Regression Systematic Error - Train Dataset Check is relevant for models of type ['regression'], but received model of type 'multiclass'
Dominant Frequency Change Nothing found
Conflicting Labels - Train Dataset Nothing found
Special Characters - Test Dataset Nothing found
Special Characters - Train Dataset Nothing found
String Length Out Of Bounds - Test Dataset Nothing found
String Length Out Of Bounds - Train Dataset Nothing found
Data Duplicates - Test Dataset Nothing found
Data Duplicates - Train Dataset Nothing found
String Mismatch - Test Dataset Nothing found
String Mismatch - Train Dataset Nothing found
Mixed Nulls - Test Dataset Nothing found
Mixed Data Types - Train Dataset Nothing found
Whole Dataset Drift Nothing found
Mixed Nulls - Train Dataset Nothing found
Single Value in Column - Test Dataset Nothing found
Conflicting Labels - Test Dataset Nothing found
String Mismatch Comparison Nothing found
New Label Train Test Nothing found
Category Mismatch Train Test Nothing found
Mixed Data Types - Test Dataset Nothing found
Single Value in Column - Train Dataset Nothing found

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.. GENERATED FROM PYTHON SOURCE LINES 86-90 Run the integrity suite ----------------------- If you still haven't started modeling and just have a single dataset, you can use the ``single_dataset_integrity``: .. GENERATED FROM PYTHON SOURCE LINES 90-96 .. code-block:: default from deepchecks.tabular.suites import single_dataset_integrity integ_suite = single_dataset_integrity() integ_suite.run(ds_train) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Single Dataset Integrity Suite: 0%| | 0/9 [00:00

Single Dataset Integrity Suite

The suite is composed of various checks such as: String Length Out Of Bounds, Outlier Sample Detection, Mixed Nulls, etc...
Each check may contain conditions (which will result in pass / fail / warning / error , represented by ✓ / ✖ / ! / ⁈ ) as well as other outputs such as plots or tables.
Suites, checks and conditions can all be modified. Read more about custom suites.

Conditions Summary

Status Check Condition More Info
✓
 Single Value in Column Does not contain only a single value
✓
 Mixed Nulls Not more than 1 different null types
✓
 Mixed Data Types Rare data types in column are either more than 10% or less than 1% of the data
✓
 String Mismatch No string variants
✓
 Data Duplicates Duplicate data ratio is not greater than 0%
✓
 String Length Out Of Bounds Ratio of outliers not greater than 0% string length outliers
✓
 Special Characters Ratio of entirely special character samples not greater than 0.1%
✓
 Conflicting Labels Ambiguous sample ratio is not greater than 0%

Check With Conditions Output

Check Without Conditions Output

Outlier Sample Detection

Detects outliers in a dataset using the LoOP algorithm.

Additional Outputs
The Outlier Probability Score is calculated by the LoOP algorithm which measures the local deviation of density of a given sample with respect to its neighbors. These outlier scores are directly interpretable as a probability of an object being an outlier (see link for more information).

  Outlier Probability Score sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
41 0.89 4.50 2.30 1.30 0.30 0
106 0.72 4.90 2.50 4.50 1.70 2
56 0.57 6.30 3.30 4.70 1.60 1
114 0.56 5.80 2.80 5.10 2.40 2
22 0.56 4.60 3.60 1.00 0.20 0

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Other Checks That Weren't Displayed

Check Reason
Single Value in Column Nothing found
Mixed Nulls Nothing found
Mixed Data Types Nothing found
String Mismatch Nothing found
Data Duplicates Nothing found
String Length Out Of Bounds Nothing found
Special Characters Nothing found
Conflicting Labels Nothing found

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.. GENERATED FROM PYTHON SOURCE LINES 97-104 Run a Deepchecks Check ====================== If you want to run a specific check, you can just import it and run it directly. Check out the :doc:`Check tabular examples ` in the examples or the :doc:`API Reference ` for more info about the existing checks and their parameters. .. GENERATED FROM PYTHON SOURCE LINES 104-107 .. code-block:: default from deepchecks.tabular.checks import TrainTestLabelDrift .. GENERATED FROM PYTHON SOURCE LINES 108-113 .. code-block:: default check = TrainTestLabelDrift() result = check.run(ds_train, ds_test) result .. raw:: html

Train Test Label Drift

Calculate label drift between train dataset and test dataset, using statistical measures.

Additional Outputs
The Drift score is a measure for the difference between two distributions, in this check - the test and train distributions.
The check shows the drift score and distributions for the label.


.. GENERATED FROM PYTHON SOURCE LINES 114-115 and also inspect the result value which has a check-dependant structure: .. GENERATED FROM PYTHON SOURCE LINES 115-118 .. code-block:: default result.value .. rst-class:: sphx-glr-script-out Out: .. code-block:: none {'Drift score': 0.002507306267756066, 'Method': 'PSI'} .. GENERATED FROM PYTHON SOURCE LINES 119-126 Edit an Existing Suite ====================== Inspect suite and remove condition ---------------------------------- We can see that the single feature contribution failed, both for test and for train. Since this is a very simple dataset with few features and this behavior is not necessarily problematic, we will remove the existing conditions for the PPS .. GENERATED FROM PYTHON SOURCE LINES 126-131 .. code-block:: default # Lets first print the suite to find the conditions that we want to change: suite .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Full Suite: [ 0: ModelInfo 1: ColumnsInfo 2: ConfusionMatrixReport 3: PerformanceReport Conditions: 0: Train-Test scores relative degradation is not greater than 0.1 4: RocReport(excluded_classes=[]) Conditions: 0: AUC score for all the classes is not less than 0.7 5: SimpleModelComparison Conditions: 0: Model performance gain over simple model is not less than 10% 6: ModelErrorAnalysis Conditions: 0: The performance difference of the detected segments must not be greater than 5% 7: CalibrationScore 8: RegressionSystematicError Conditions: 0: Bias ratio is not greater than 0.01 9: RegressionErrorDistribution Conditions: 0: Kurtosis value is not less than -0.1 10: BoostingOverfit Conditions: 0: Test score over iterations doesn't decline by more than 5% from the best score 11: UnusedFeatures Conditions: 0: Number of high variance unused features is not greater than 5 12: ModelInferenceTime Conditions: 0: Average model inference time for one sample is not greater than 0.001 13: TrainTestFeatureDrift Conditions: 0: PSI <= 0.2 and Earth Mover's Distance <= 0.1 14: TrainTestLabelDrift Conditions: 0: PSI <= 0.2 and Earth Mover's Distance <= 0.1 for label drift 15: WholeDatasetDrift Conditions: 0: Drift value is not greater than 0.25 16: DominantFrequencyChange Conditions: 0: Change in ratio of dominant value in data is not greater than 25% 17: CategoryMismatchTrainTest Conditions: 0: Ratio of samples with a new category is not greater than 0% 18: NewLabelTrainTest Conditions: 0: Number of new label values is not greater than 0 19: StringMismatchComparison Conditions: 0: No new variants allowed in test data 20: DatasetsSizeComparison Conditions: 0: Test-Train size ratio is not smaller than 0.01 21: DateTrainTestLeakageDuplicates Conditions: 0: Date leakage ratio is not greater than 0% 22: DateTrainTestLeakageOverlap Conditions: 0: Date leakage ratio is not greater than 0% 23: SingleFeatureContributionTrainTest(ppscore_params={}) Conditions: 0: Train-Test features' Predictive Power Score difference is not greater than 0.2 1: Train features' Predictive Power Score is not greater than 0.7 24: TrainTestSamplesMix Conditions: 0: Percentage of test data samples that appear in train data not greater than 10% 25: IdentifierLeakage(ppscore_params={}) Conditions: 0: Identifier columns PPS is not greater than 0 26: IndexTrainTestLeakage Conditions: 0: Ratio of leaking indices is not greater than 0% 27: IsSingleValue Conditions: 0: Does not contain only a single value 28: MixedNulls Conditions: 0: Not more than 1 different null types 29: MixedDataTypes Conditions: 0: Rare data types in column are either more than 10% or less than 1% of the data 30: StringMismatch Conditions: 0: No string variants 31: DataDuplicates Conditions: 0: Duplicate data ratio is not greater than 0% 32: StringLengthOutOfBounds Conditions: 0: Ratio of outliers not greater than 0% string length outliers 33: SpecialCharacters Conditions: 0: Ratio of entirely special character samples not greater than 0.1% 34: ConflictingLabels Conditions: 0: Ambiguous sample ratio is not greater than 0% 35: OutlierSampleDetection ] .. GENERATED FROM PYTHON SOURCE LINES 132-137 .. code-block:: default # now we can use the check's index and the condition's number to remove it: print(suite[6]) suite[6].remove_condition(0) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ModelErrorAnalysis Conditions: 0: The performance difference of the detected segments must not be greater than 5% .. GENERATED FROM PYTHON SOURCE LINES 138-142 .. code-block:: default # print and see that the condition was removed suite[6] .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ModelErrorAnalysis .. GENERATED FROM PYTHON SOURCE LINES 143-151 If we now re-run the suite, all of the existing conditions will pass. *Note: the check we manipulated will still run as part of the Suite, however it won't appear in the Conditions Summary since it no longer has any conditions defined on it. You can still see its display results in the Additional Outputs section* **For more info about working with conditions, see the detailed configuring conditions guide.** .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.264 seconds) .. _sphx_glr_download_user-guide_tabular_auto_tutorials_plot_quickstart_in_5_minutes.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_quickstart_in_5_minutes.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_quickstart_in_5_minutes.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_