| Name |
Description |
| Anomaly |
An anomaly detector is a predictive model that can help identify the instances within a dataset that do not conform to a regular pattern. It can be useful for tasks like data cleansing, identifying unusual instances, or, given a new data point, deciding whether a model is competent to make a prediction or not. The complete and updated reference with all available parameters is in our documentation website. |
| AnomalyScore |
An anomaly score is created from an anomaly and the input_data for which you wish to create an anomaly score. New anomaly score will be between 0 and 1. The complete and updated reference with all available parameters is in our documentation website. |
| Association |
Association Discovery is a popular method to find out relations among values in high-dimensional datasets. It is commonly used for basket market analysis. This analysis seeks for customer shopping patterns across large transactional datasets. For instance, do customers who buy hamburgers and ketchup also consume bread? Businesses use those insights to make decisions on promotions and product placements. Association Discovery can also be used for other purposes such as early incident detection, web usage analysis, or software intrusion detection. The complete and updated reference with all available parameters is in our documentation website. |
| BatchAnomalyScore |
A batch anomaly score provides an easy way to compute an anomaly score for each instance in a dataset in only one request. To create a new batch anomaly score you need an anomaly/id and a dataset/id. The complete and updated reference with all available parameters is in our documentation website. |
| BatchCentroid |
A batch centroid provides an easy way to compute a centroid for each instance in a dataset in only one request. To create a new batch centroid you need a cluster/id and a dataset/id. The complete and updated reference with all available parameters is in our documentation website. |
| BatchPrediction |
A batch prediction provides an easy way to compute a prediction for each instance in a dataset in only one request. To create a new batch prediction you need a model/id or an ensemble/id or a logisticregression/id and a dataset/id. The complete and updated reference with all available parameters is in our documentation website. |
| BatchTopicDistribution |
A batch topic distribution provides an easy way to compute a topic distribution for each instance in a dataset in only one request. To create a new batch topic distribution you need a topicmodel/id and a dataset/id. The complete and updated reference with all available parameters is in our documentation website. |
| Centroid |
A centroid is created using a cluster/id and the new instance (input_data) for which you wish to create a centroid. The complete and updated reference with all available parameters is in our documentation website. |
| Client |
|
| Cluster |
A cluster is a set of groups (i.e., clusters) of instances of a dataset that have been automatically classified together according to a distance measure computed using the fields of the dataset. Each group is represented by a centroid or center that is computed using the mean for each numeric field and the mode for each categorical field. The complete and updated reference with all available parameters is in our documentation website. |
| Constants |
|
| Correlation |
A correlation resource allows you to compute advanced statistics for the fields in your dataset by applying various exploratory data analysis techniques to compare the distributions of the fields in your dataset against an objective_field. The complete and updated reference with all available parameters is in our documentation website. |
| DataSet |
A dataset is a structured version of a source where each field has been processed and serialized according to its type. The complete and updated reference with all available parameters is in our documentation website. |
| DataSet.Field.Summary |
Statistical summary of field |
| DataSet.Field.Summary.Datetime |
Text summaries give you a count per each category and missing count in case any of the instances contain missing values. |
| DataSet.Field.Summary.Items |
Items summaries give you a count per each category and missing count in case any of the instances contain missing values. |
| DataSet.Field.Summary.Text |
Text summaries give you a count per each category and missing count in case any of the instances contain missing values. |
| Ensemble |
An ensemble is a number of models grouped together to create a stronger model with better predictive performance. The complete and updated reference with all available parameters is in our documentation website. |
| Ensemble.LocalEnsemble |
|
| Evaluation |
An evaluation provides an easy way to measure the performance of a predictive model. To create a new evaluation you need a model/id or an ensemble/id and a dataset/id. The complete and updated reference with all available parameters is in our documentation website. |
| Execution |
Once a WhizzML script has been created, you can execute it as many times as you want. Every execution will return a list of outputs and/or BigML resources (models, ensembles, clusters, predictions, etc.) that were created during the given run. It's also possible to execute a pipeline of more than one scripts in one request. The complete and updated reference with all available parameters is in our documentation website. |
| Library |
A library is a special kind of compiled WhizzML source code that only defines functions and constants. It is intended as an import for executable scripts. The complete and updated reference with all available parameters is in our documentation website. |
| LogisticRegression |
A logistic regression is a supervised machine learning method for solving classification problems. The probability of the objective being a particular class is modeled as the value of a logistic function, whose argument is a linear combination of feature values. The complete and updated reference with all available parameters is in our documentation website. |
| MissingStrategy |
|
| Model |
A model is a tree-like representation of your dataset with predictive power. You can create a model selecting which fields from your dataset you want to use as input fields (or predictors) and which field you do want to predict, the objective field. The complete and updated reference with all available parameters is in our documentation website. |
| Model.LocalModel |
|
| Model.Node |
A tree-like recursive structure representing the model. |
| Model.Predicate |
Predicate structure to make a decision at each node. |
| Project |
A project is an abstract resource that helps you group related BigML resources together. A project must have a name and optionally a category, description, and tags to help you organize and retrieve it. The complete and updated reference with all available parameters is in our documentation website. |
| Response |
Base class for response from BigML |
| Sample |
A sample provides fast-access to the raw data of a dataset on an on-demand basis. The complete and updated reference with all available parameters is in our documentation website. |
| Script |
A script is compiled source code written in WhizzML, BigML's custom scripting language for automating Machine Learning workflows. Once a script has been created and compiled, it can be used as an input for an execution resource. The complete and updated reference with all available parameters is in our documentation website. |
| Source |
A data source or source is the raw data that you want to use to create a predictive model, detect their anomalies, etc. The complete and updated reference with all available parameters is in our documentation website. |
| StatisticalTest |
A statistical test resource automatically runs some advanced statistical tests on the numeric fields of a dataset. The goal of these tests is to check whether the values of individual fields or differ from some distribution patterns. Statistical test are useful in tasks such as fraud, normality, or outlier detection. The complete and updated reference with all available parameters is in our documentation website. |
| TopicModel |
A topic model is an unsupervised machine learning method for unveiling all the different topics underlying a collection of documents. BigML uses Latent Dirichlet allocation (LDA), one of the most popular probabilistic methods for topic modeling. The complete and updated reference with all available parameters is in our documentation website. |
| Utils |
|