Glossary¶

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Autoencoder

An extension of the Deep Learning framework. Can be used to compress input features (similar to PCA). Sparse autoencoders are simple extensions that can increase accuracy. Use autoencoders for:

generic dimensionality reduction (for pre-processing for any algorithm)
anomaly detection (for comparing the reconstructed signal with the original to find differences that may be anomalies)
layer-by-layer pre-training (using stacked auto-encoders)

Backpropogation

Uses a known, desired output for each input value to calculate the loss function gradient for training. If enabled, performed after each training sample in Deep Learning.

BAD

A column type that contains only missing values.

Balance classes

A parameter that oversamples the minority classes to balance the distribution.

Batch size: The number of training examples in one forward/backward pass. The higher the batch size, the more memory space you’ll need. (See also epoch and iteration.)
Beta constraints: A data.frame or H2OParsedData object with the columns [“names”, “lower_bounds”,”upper_bounds”, “beta_given”], where each row corresponds to a predictor in the GLM. “names” contains the predictor names, “lower_bounds” and “upper_bounds” are the lower and upper bounds of beta, and “beta_given” is some supplied starting values for beta.
Binary: A variable with only two possible outcomes. Refer to binomial.

Binomial: A variable with the value 0 or 1. Binomial variables assigned as 0 indicate that an event hasn’t occurred or that the observation lacks a feature, where 1 indicates occurrence or instance of an attribute.
Bins: Bins are linear-sized from the observed min-to-max for the subset that is being split. Large bins are enforced for shallow tree depths. Based on the tree decisions, as the tree gets deeper, the bins are distributed symmetrically over the reduced range of each subset.

Categorical: A qualitative, unordered variable (for example, A, B, AB, and O would be values for the category blood type); synonym for enumerator or factor. Stored as an int column with a String mapping in H2O; limited to 10 million unique strings in H2O.

Classification: A model whose goal is to predict the category for the response column input.
Clip: In the H2O web UI Flow, a clip is a single cell in a flow containing an action that is saved for later reuse.

Cloud: Synonym for cluster. Refer to the definition for cluster.

Cluster

A group of H2O nodes that work together; when a job is submitted to a cluster, all the nodes in the cluster work on a portion of the job. Synonym for cloud.
In statistics, a cluster is a group of observations from a data set identified as similar according to a particular clustering algorithm.

Confusion matrix: Table that depicts the performance of the algorithm (using the false positive rate, false negative, true positive, and true negative rates).

Continuous: A variable that can take on all or nearly all values along an interval on the real number line (for example, height or weight). The opposite of a discrete value, which can only take on certain numerical values (for example, the number of patients treated).
CSV file: CSV is an initialism for comma-separated value. A CSV file stores data in a plain text format.
Data frame: A distributed representation of a large dataset.

Deep Learning: Uses a composition of multiple non-linear transformations to model high-level abstractions in data.

Dependent variable: The response column in H2O; what you are trying to measure, observe, or predict. The opposite of an independent variable.
Destination key: Automatically generated key for a model that allows recall of a specific model later in analysis. Users can specify a different destination key than the key generated by H2O.
Deviance: Deviance is the difference between an expected value and an observed value. It plays a critical role in defining GLM models. For a more detailed discussion of deviance, please refer to the H2O Data Science documentation on GLM.

Distributed key/value (DKV): Distributed key/value store. Refer also to key/value store.

Discrete: A variable that can only take on certain numerical values (for example, the number of patients treated). The opposite of a continuous variable.

Enumerator/enum: A data type where the value is one of a defined set of named values known as “elements”, “members”, or “enumerators.” For example, cat, dog, & mouse are enumerators of the enumerated type animal.

Epoch: A measure of the number of times all of the training vectors are passed forward and backward once to update the weights. For batch training all of the training samples pass through the learning algorithm simultaneously in one epoch before weights are updated. For sequential training all of the weights are updated after each training vector is sequentially passed through the training algorithm. (See also Batch size and iteration.)

Factor: A data type where the value is one of a defined set of categories. Refer to enumerator and categorical.
Family: The distribution options available for predictive modeling in GLM.
Feature: Synonym for attribute, predictor, or independent variable. Usually refers to the data observed on features given in the columns of a data set.
Feed-forward: Associates input with output for pattern recognition.
Flatfile: A basic text file containing multiple IP addresses (one per line) used by H2O to configure a cluster.
Flow: Refers to the series of cell-based actions created in H2O’s web UI or the web UI itself.
Gzipped (gz) file: Gzip is a type of file compression commonly used for H2O file dependencies.
HEX format: Records made up of hexadecimal numbers representing machine language code or constant data. In H2O, data must be parsed into .hex format before you can perform operations on it.
Hit ratio: (Multinomial only) The number of times the prediction was correct out of the total number of predictions.

Independent variable: The factors can be manipulated or controlled (also known as predictors). The opposite of a dependent variable.
Instance: Occurs each time H2O is started. This process builds a cluster of nodes (even if it is only a one-node cluster on a local machine). The instance begins when the cluster is formed and ends when the program is closed.
Integer: A whole number (can be negative but cannot be a fraction). Can be represented in H2O as an int, which is not a type but a property of the data.

Iteration: Number of passes during training or testing, with each pass using [batch size] number of examples. (Note that a pass includes a forward pass and a backward pass. The forward pass and backward pass do not count as two different passes.) For example, if you have 1000 training examples, and your batch size is 500, then it will take 2 iterations to complete 1 epoch. (See also Batch size and epoch.)
Job: A task performed by H2O. For example, reading a data file, parsing a data file, or building a model. In the browser-based GUI of H2O, each job is listed in the Admin menu under Jobs.
JVM: Java virtual machine; used to run H2O.
Key: The .hex key generated when data are parsed into H2O. In the web-based GUI, key is an input on each page where users define models and any page where users validate models on a new data set or use a model to generate predictions.
Key/value pair: A type of data that associates a particular key index to a certain datum.

Key/value store: A tool that allows storage of schema-less data. Data usually consists of a string that represents the key, and the data itself, which is the value. Refer also to distributed key/value.
L1 regularization: A regularization method that constrains the absolute value of the weights and has the net effect of dropping some values (setting them to zero) from a model to reduce complexity and avoid overfitting.
L2 regularization: A regularization method that constrains the sum of the squared weights. This method introduces bias into parameter estimates but frequently produces substantial gains in modeling as estimate variance is reduced.
Link function: A user-defined option in GLM.
Loss function: The function minimized in order to achieve a desired estimator; synonymous to objective function and criterion function. For example, linear regression defines the set of best parameter estimates as the set of estimates that produces the minimum of the sum of the squared errors. Errors are the difference between the predicted value and the observed value.
MSE: Mean squared error; measures the average of the squares of the error rate (the difference between the predictors and what was predicted).
Multinomial: A variable where the value can be one of more than two possible outcomes (for example, blood type).
N-folds: User-defined number of cross validation models generated by H2O.
Node: In distributed computing systems, nodes include clients,servers, or peers. In statistics, a node is a decision or terminal point in a classification tree.
Numeric: A column type containing real numbers, small integers, or booleans.
Offset: A parameter that compensates for differences in units of observation (for example, different populations or geographic sizes) to make sure outcome is proportional.
Outline: In H2O’s web UI Flow, a brief summary of the actions contained in the cells.
Parse: Analysis of a string of symbols or datum that results in the conversion of a set of information from a person-readable format to a machine-readable format.
POJO: Plain Old Java Object; a way to export a model built in H2O and implement it in a Java application.
Regression: A model where the input is numerical and the output is a prediction of numerical values. Also known as “quantitative”; the opposite of a classification model.

Response column: Method of selecting the dependent variable in H2O.
Real: A fractional number.
ROC Curve: Graph representing the ratio of true positives to false negatives.
Scoring history: Represents the error rate of the model as it is built.
Seed: A starting point for randomization. Seed specification is used when machine learning models have a random component; it allows users to recreate the exact “random” conditions used in a model at a later time.
Separator: What separates the entries in the dataset; usually a comma, semicolon, etc.
Sparse: A dataset where many of the rows contain blank values or “NA” instead of data.
Standard deviation: The standard deviation of the data in the column, defined as the square root of the sum of the deviance of observed values from the mean divided by the number of elements in the column minus one. Abbreviated sd.
Standardization: Transformation of a variable so that it is mean-centered at 0 and scaled by the standard deviation; helps prevent precision problems.
String: Refers to data where each entry is typically unique (for example, a dataset containing people’s names and addresses).
Supervised learning: Model type where the input is labeled so that the algorithm can ideitify it and learn from it.
Time: Data type supported by H2O; represented as “milliseconds-since-the-Unix-Epoch”; stored internally as a 64-bit integer in a standard int column. Used directly by the Cox Proportional Hazards model, but also used to build other features.
Training frame: The dataset used to build the model.
Unsupervised learning: Model type where the input is not labeled.
UUID: A dense representation of universally unique identifiers (UUIDs) used to label and group events; stored as a 128-bit numeric value.
Validation: An analysis of how well the model fits.
Validation frame: The dataset used to evaluate the accuracy of the model.
Variable importance: Represents the statistical significance of each variable in the data in terms of its affect on the model.
Weights: A parameter that specifies certain outcomes as more significant (for example, if you are trying to identify incidence of disease, one “positive” result can be more meaningful than 50 “negative” responses). Higher values indicate more importance.
XLS file: A Microsoft Excel 2003-2007 spreadsheet file format.
Y: Dependent variable used in GLM; a user-defined input selected from the set of variables present in the user’s data.
YARN: Yet Another Resource Manager; used to manage H2O on a Hadoop cluster.