Analysis Report
- class mlbugdetection.analysis_report.AnalysisReport[source]
Bases:
objectAnalysis Report Class All library functions returns a Analysis Report Object
- Parameters
model_name (str, default = '') – Name of the model being analysed.
analysed_feature (str, default = '') – Name of the feature being analysed.
feature_range (tuple, default = ()) – Range of values of the feature being analysed: (start, stop).
metrics (dictionary, default = {}) –
Dictionary with all the calculated metrics. All the possible metrics that can be calculated are:
- ’monotonic’bool
If the list of values is monotonic.
- ’monotonic_mse’: float
MSE between the list of values and it`s closest monotonic aproximation.
- ’positive_changes_ranges’List
List of feature ranges that resulted in the biggest positive changes in the model`s prediction probability.
- ’positive_changes_proba’List
List of biggest positive variations in the model`s prediction probability.
- ’negative_changes_ranges’List
List of feature ranges that resulted in the biggest negative changes in the model`s prediction probability.
- ’negative_changes_proba’List
List of biggest negative variations in the model`s prediction probability.
- ’classification_change_ranges’List
List of feature ranges that resulted in a change of the model`s classification.
- ’classification_change_proba’List
List of prediction probability values before and after the classification change.
- ’positive_means’dictionary
Contains the following:
- ’mean’float
Mean of the all the positive changes means
- ’median’float
Median of the all the positive changes means
- ’std’float
Standard Deviation of the all the positive changes means
- ’var’float
Variation of the all the positive changes means
- ’negative_means’dictionary
Same as “positive_means”, but for negative variations in the prediction probabilities.
- ’sanity’bool
If the model is sane or not.
- ’sanity_indexes’List
List of indexes of the samples that were misclassified.
graphs (List, default = []) – List of all the figures created.