1.	##### **Model Interpretability**
  *	Model Respecification 
	
Protected Values Prediction
Model Constraints
Hyperparameter Modelling
Interpretable Model
Global Explanations
Level Two Monotonicity Feature Explanations


  *	Quantitative Validation 
Level Two Monotonicity
Relationship Analysis
Partial Dependence (LV1) Monotonicity
Feature Interaction
Metrics

1.	##### **Model Robustness**
  *	Residual Deviation
  *	Residual Explanations
  *	Benchmark Competition
  *	Adversarial Attack

1.	##### **Regulatory Fairness**
  *	Group
Disparate Error Analysis
	Parity Indicators
	Fair Lending Measures
Model Agnostic Processing
	Reweighing Preprocessing
	Disparate Impact Preprocessing
	Calibrate Equalized Odds
Feature Decomposition	
  *	Individual
        1. Reasoning
            * Individual Disparity
            * Reasoning Codes
        1. Example Based
	    * Prototypical
            * Counterfactual
            * Contrastive






#### [Table Processing](#scrollTo=VO6NxvYjofas&line=1&uniqifier=1)
  -	[Configure Pandas](#configure-pandas)
  -	[Data Frame Formatting](#data-frame-formatting)
  -	[Data Frames for Testing](#data-frames-for-testing)
  -	[Lower Case Columns](#lower-case-columns)
  -	[Front and Back Column Selection](#front-and-back-columns)
  -	[Fast Data Frame Split](#fast-data-frame-split)
  -	[Create Features and Labels List](#create-features-and-labels-list)
  -	[Short Basic Commands](#short-basic-commands)
  -	[Read Commands](#read-commands)
  -	[Create Ordered Categories](#create-ordered-categories)
  -	[Select Columns Based on Regex](#select-columns-based-on-regex)
  -	[Accessing Group of Groupby Object](#accessing-group-of-groupby-object)
  -	[Multiple External Selection Criteria](#multiple-external-selection-criteria)
  -	[Memory Reduction Script](#memory-reduction-script)
  -	[Verify Primary Key](#verify-primary-key)
  -	[Shift Columns to Front](#shift-columns-to-front)
  -	[Multiple Column Assignment](#multiple-column-assignment)
  -	[Method Changing Technique](#method-chaning-event)
  -	[Load Multiple Files](#load-multiple-files)
  -	[Drop Rows and Column Substring](#drop-rows-and-column-substring)
  -	[Explode a Column](#explode-a-column)
  -	[Nest List Back into Column](#nest-list-back-into-column)
  -	[Split Cells with List](#split-cells-with-list)


#### [Table Exploration](#table-exploration)

  -	[Groupby Functionality](#groupby-functionality)
  -	[Cross Correlation Series Without Duplicates](#cross-correlation-series-without-duplicates)
  -	[Missing Data Report](#missing-data-report)
  -	[Duplicated Rows Report](#duplicated-rows-report)
  -	[Skewness](#skewness)


#### [Feature Processing](#feature-processing)

  -	[Replace Infrequently Occurring Categories](#replace-infrequently-occuring-categories)
  -	[Quasi-Constant Feature Detection](#quasi-constant-feature-detection)
  -	[Filling Missing Values Separately](#filling-missing-values-separately)
  -	[Conditioned Column Value Replacement](#conditioned-column-value-replacement)
  -	[Remove Non-numeric Values in Data Frame](#remove-non-numeric-values-in-data-frame)
  -	[Feature Scaling, Normalisation, Standardisation](#feature-scaling-normalisation-standardisation)


#### [Feature Engineering](#feature-engineering)

  -	[Automated Dummy Encoding](#automate-dummy-encodings)
  -	[Binarise Empty Columns](#binarise-empty-columns)
  -	[Polynomials](#polynomials)
  -	[Transformations](#transformations)
  -	[Genetic Programming](#genetic-programming)
  -	[Principal Component](#principal-component)
  -	[Multiple Lags](#multiple-lags)
  -	[Multiple Rolling](#multiple-rolling)
  -	[Date Features](#data-features)
  -	[Haversine Distance](#havervsine-distance)
  -	[Parse Address](#parse-address)
  -	[Processing Strings in Pandas](#processing-strings-in-pandas)
  -	[Filtering Strings in Pandas](#filtering-strings-in-pandas)


#### [Model Validation](#scrollTo=VO6NxvYjofas&line=1&uniqifier=1)

  -	[Classification Metrics](#classification-metrics)





I currently have you listed as a potential contributing member to Medium FirmAI.Is this something you would be interested in? For contributors - https://medium.com/firmai/example-3a-short-format-bulletin-7cbd94d8754c

. Our first publications would be from Vasant Dhar and Foster Provost. Is this something you would be interested in? For contributors - https://medium.com/firmai/example-3a-short-format-bulletin-7cbd94d8754c and for quality examples https://medium.com/@derek_snow/quality-post-examples-f52a887ecb35

I currently have you listed as a potential contributing member to Medium FirmAI. Is this something you would be interested in? Contributing Fields - https://medium.com/firmai/example-3a-short-format-bulletin-7cbd94d8754c  - Our first contributors are Vasant Dhar, Foster Provost, and Isil Erel.

Hi, I listed you as a potential contributor to Medium FirmAI. Is this something you would be interested in? Contributing Fields - https://medium.com/firmai/example-3a-short-format-bulletin-7cbd94d8754c  - Our first contributors are Vasant Dhar, Foster Provost, and Isil Erel; starting 4 May.












#### overall performance
'Prevalence   ': '(tp + fn) / (tp + tn +fp + fn)', # how much default actually happens for this group
#'Adverse Impact': '(tp + fp) / (tp + tn + fp + fn)', # how often the model predicted default for each group   
'Accuracy   ':       '(tp + tn) / (tp + tn + fp + fn)', # how often the model predicts default and non-default correctly for this group

#### predicting default will happen
# (correctly)
'True Positive Rate   ': 'tp / (tp + fn)',  # out of the people in the group *that did* default, how many the model predicted *correctly* would default              
'Precision   ':          'tp / (tp + fp)',  # out of the people in the group the model *predicted* would default, how many the model predicted *correctly* would default

#### predicting default won't happen
# (correctly)
'Specificity   ':              'tn / (tn + fp)', # out of the people in the group *that did not* default, how many the model predicted *correctly* would not default
'Negative Predicted Value   ': 'tn / (tn + fn)', # out of the people in the group the model *predicted* would not default, how many the model predicted *correctly* would not default  

#### analyzing errors - type I
# false accusations 
'False Positive Rate   ':  'fp / (tn + fp)', # out of the people in the group *that did not* default, how many the model predicted *incorrectly* would default
'False Discovery Rate   ': 'fp / (tp + fp)', # out of the people in the group the model *predicted* would default, how many the model predicted *incorrectly* would default

#### analyzing errors - type II
# costly ommisions
'False Negative Rate   ': 'fn / (tp + fn)', # out of the people in the group *that did* default, how many the model predicted *incorrectly* would not default
'False Omissions Rate   ':'fn / (tn + fn)'  # out of the people in the group the model *predicted* would not default, how many the model predicted *incorrectly* would not default
}