AutoAI implementation details
AutoAI automatically prepares data, applies algorithms, or estimators, and builds model pipelines best suited for your data and use case.
This topic describes some of these technical details that go into generating the pipelines:
 Preparing and preprocessing the data
 Algorithms used for classification models
 Algorithms used for regression models
 Metrics by model type
 Data transformations
 AutoAI FAQ
Preparing the data for training
During automatic data preparation, AutoAI analyzes the training data and prepares it for model selection and pipeline generation. Data preparation involves these steps:
Feature column classification
 Detects the types of feature columns and classifies them as categorical or numerical class
 Detects various types of missing values (default, userprovided, outliers)
Feature engineering
 Handles rows for which target values are missing (drop (default) or target imputation)
 Drops unique value columns (except datetime and timestamps)
 Drops constant value columns
Preprocessing (data imputation and encoding)
 Applies Sklearn imputation/encoding/scaling strategies (separately on each feature class)
 Handles labels of test set that were not seen in training set
Algorithms used for classification models
These algorithms are the default algorithms used for automatic model selection for classification problems.
Algorithm  Description 

Decision Tree Classifier  Maps observations about an item (represented in branches) to conclusions about the item’s target value (represented in leaves). Supports both binary and multiclass labels, as well as both continuous and categorical features. 
Extra Trees Classifier  An averaging algorithm based on randomized decision trees. 
Gradient Boosted Tree Classifier  Produces a classification prediction model in the form of an ensemble of decision trees. It only supports binary labels, as well as both continuous and categorical features. 
LGBM Classifier  Gradient boosting framework that uses leafwise (horizontal) treebased learning algorithm. 
Logistic Regression  Analyzes a data set in which there are one or more independent variables that determine one of two outcomes. Only binary logistic regression is supported 
Random Forest Classifier  Constructs multiple decision trees to produce the label that is a mode of each decision tree. It supports both binary and multiclass labels, as well as both continuous and categorical features. 
XGBoost Classifier  Accurate sure procedure that can be used for classification problems. XGBoost models are used in a variety of areas including Web search ranking and ecology. 
Algorithms used for regression models
These algorithms are the default algorithms used for automatic model selection for regression problems.
Algorithm  Description 

Decision Tree Regression  Maps observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the leaves). It supports both continuous and categorical features. 
Extra Trees Regression  An averaging algorithm based on randomized decision trees. 
Gradient Boosting Regression  Produces a regression prediction model in the form of an ensemble of decision trees. It supports both continuous and categorical features. 
LGBM Regression  Gradient boosting framework that uses treebased learning algorithms. 
Linear Regression  Models the linear relationship between a scalardependent variable y and one or more explanatory variables (or independent variables) x. 
Random Forest Regression  Constructs multiple decision trees to produce the mean prediction of each decision tree. It supports both continuous and categorical features. 
Ridge  Ridge regression is similar to Ordinary Least Squares but imposes a penalty on the size of coefficients. 
XGBoost Regression  GBRT is an accurate and effective offtheshelf procedure that can be used for regression problems. Gradient Tree Boosting models are used in a variety of areas including Web search ranking and ecology. 
Metrics by model type
The following metrics are available for measuring the accuracy of pipelines during training and when scoring data.
Binary classification metrics
 Accuracy (default for ranking the pipelines)
 Roc auc
 Average precision
 F
 Negative log loss
 Precision
 Recall
Multiclass classification metrics
Metrics for multiclass models can be adjusted to account for imbalances in labels. for example:
 Metrics with the micro qualifier calculate metrics globally by counting the total true positives, false negatives and false positives.
 Metrics with the micro qualifier calculates metrics for each label, and finds their unweighted mean. This does not take label imbalance into account.
 Metrics with the weighted qualifier calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters macro to account for label imbalance; it can result in an Fscore that is not between precision and recall.
These are the multiclass classification metrics:
 Accuracy (default for ranking the pipelines)
 F1
 F1 Micro
 F1 Macro
 F1 Weighted
 Precision
 Precision Micro
 Precision Macro
 Precision Weighted
 Recall
 Recall Micro
 Recall Macro
 Recall Weighted
Regression metrics
 Negative root mean squared error (default for ranking the pipeline)
 Negative mean absolute error
 Negative root mean squared log error
 Explained variance
 Negative mean squared error
 Negative mean squared log error
 Negative median absolute error
 R2
Metrics used for feature importance
Feature importance is calculated from the average of nine measures applied to the training data:
 Linear Correlation (f_regression) metric
 Maximal Information Coefficient (MIC) metric
 Linear Regression (LR) metric
 L1 regularization metric (Lasso)
 Ridge metric
 RF metric
 Stability Selection
 Recursive Feature Elimination (RFE)
 Recursive Feature Elimination plus selection of best number of features
Data transformations
For feature engineering, AutoAI uses a novel approach that explores various feature construction choices in a structured, nonexhaustive manner, while progressively maximizing model accuracy using reinforcement learning. This results in an optimized sequence of transformations for the data that best match the algortihms, or estimators, of the model selection step. This table lists some of the transformations used and some wellknown conditions under which they are useful. This is not an exhaustive list of scenarios where the transformation is useful, as that can be complex and hard to interpret. Finally, the listed scenarios are not an explanation of how the transformations are selected. The selection of which transforms to apply is done in a trial and error, performanceoriented manner.
Name  Code  Function 

Principle Component Analysis  pca  Reduce dimensions of data and realign across a more suitable coordinate system. Helps tackle the ‘curse of dimensionality’ in linearly correlated data. It eliminates redundancy and separates significant signals in data. 
Standard Scaler  stdscaler  Scales data features to a standard range.This helps the efficacy and efficiency of certain learning algorithms as well as other transformations such as PCA. 
Logarithm  log  Reduces right skewness in features and make them more symmetric. Resulting symmetry in features helps algorithms understand the data better. Even scaling based on mean and variance is more meaningful on symmetrical data. Additionally, it can capture specific physical relationships between feature and target best described through a logarithm. 
Cube Root  cbrt  Reduces right skewness in data like logarithm, but is weaker than log in its impact, which might be more suitable in some cases. It is also applicable to negative or zero values to which log doesn’t apply. Cube root can also change units such as reducing volume to length. 
Square root  sqrt  Reduces mild right skewness in data. It is weaker than log or cube root. It works with zeroes and reduces spatial dimensions such as area to length. 
Square  square  Reduces left skewness to a moderate extent to make such distributions more symmetric.It can also be helpful in capturing certain phenomena such as superlinear growth. 
Product  product  A product of two features can expose a nonlinear relationship to better predict the target value than the individual values alone. For example, item cost into number of items sold is a better indication of the size of a business than any of those alone. 
Numerical XOR  nxor  This transform helps capture “exclusive disjunction” type of relationships between variables, similar to a bitwise XOR, but in a general numerical context. 
Sum  sum  Sometimes the sum of two features is better correlated to the prediction target than the features alone. For instance, loans from different sources, when summed up, provide a better idea of a credit applicant’s total indebtedness. 
Divide  divide  Division is a fundamental operand used to express quantities such as gross GDP over population (per capita GDP), representing a country’s average lifespan better than either GDP alone or population alone. 
Maximum  max  Take the higher of two values. 
Rounding  round  This transformation can be seen as perturbation or adding some noise to reduce overfitting that might have been a result of inaccurate observations. 
Absolute Value  abs  Consider only the magnitude and not the sign of observation. Sometimes, the direction or sign of an observation doesn’t matter so much as the magnitude of it, such as physical displacement, while considering fuel or time spent in the actual movement. 
Hyperbolic tangent  tanh  Nonlinear activation function can improve prediction accuracy, similar to that of neural network activation functions. 
Sine  sin  Can reorient data to discover periodic trends such as simple harmonic motions. 
Cosine  cos  Can reorient data to discover periodic trends such as simple harmonic motions. 
Tangent  tan  Trigonometric tangent transform is usually helpful in combination with other transforms. 
Feature Agglomeration  featureagglomeration  Clustering different features into groups, based upon distance or affinity, provides ease of classification for the learning algorithm. 
Sigmoid  sigmoid  Nonlinear activation function can improve prediction accuracy, similar to that of neural network activation functions. 
Isolation Forest  isoforestanomaly  Performs clustering by using an Isolation Forest to create a new feature containing an anomaly score for each sample. 
AutoAI FAQs
The following are commonly asked questions about creating an AutoAI experiment.
How many pipelines are created?
Two AutoAI parameters determine the number of pipelines:

max_num_daub_ensembles: Maximum number (topK ranked by DAUB model selection) of the selected algorithm, or estimator types, for example LGBMClassifierEstimator, XGBoostClassifierEstimator, or LogisticRegressionEstimator to use in pipeline composition. The default is 1, where only the highest ranked by model selection algorithm type is used.

num_folds: Number of subsets of the full dataset to train pipelines in addition to the full dataset. The default is 1 for training the full data set. For each fold and algorithm type, AutoAI creates 4 pipelines of increased refinement, corresponding to:
 Pipeline with default sklearn parameters for this algorithm type,
 Pipeline with optimized algorithm using HPO
 Pipeline with optimized feature engineering
 Pipeline with optimized feature engineering and optimized algorithm using HPO
The total number of pipelines generated is :
TotalPipelines= max_num_daub_ensembles * 4, if num_folds = 1:
TotalPipelines= (num_folds+1) * max_num_daub_ensembles * 4, if num_folds > 1 :
What hyperparameter optimization is applied to my model?
AutoAI uses a modelbased, derivativefree global search algorithm, called RBfOpt, which is tailored for the costly machine learning model training and scoring evaluations required by hyperparameter optimization (HPO). In contrast to Bayesian optimization, which fits a Gaussian model to the unknown objective function, RBfOpt fits a radial basis function mode to accelerate the discovery of hyperparameter configurations that maximize the objective function of the machine learning problem at hand. This acceleration is achieved by minimizing the number of expensive training and scoring machine learning models evaluations and by eliminating the need to compute partial derivatives.
For each fold and algorithm type, AutoAI creates two pipelines that use HPO to optimize for the algorithm type.
 The first is based on optimizing this algorithm type based on the preprocessed (imputed/encoded/scaled) dataset (pipeline 2) above).
 The second is based on optimizing the algorithm type based on optimized feature engineering of the preprocessed (imputed/encoded/scaled) data set.
The parameter values of the algorithms of all pipelines generated by AutoAI is published in status messages.
For more details regarding the RbfOpt algorithm, see: