Brainome's primary interface is the command line.
This notebook assumes brainome as installed per notebook brainome_101_Quick_Start
!python3 -m pip install brainome --quiet
!brainome --version
Ever forget a command parameter? Want to know what else we can do?
!brainome --help
usage: brainome [-h] [-version] [-headerless] [-target TARGET] [-ignorecolumns IGNORECOLUMNS] [-rank [ATTRIBUTERANK]] [-measureonly] [-f FORCEMODEL] [-nosplit] [-split FORCESPLIT] [-nsamples NSAMPLES] [-ignoreclasses IGNORELABELS] [-usecolumns IMPORTANTCOLUMNS] [-o OUTPUT] [-v] [-q] [-y] [-e EFFORT] [-biasmeter] [-novalidation] [-balance] [-O OPTIMIZE] [-nofun] [-modelonly] input [input ...] Brainome Table Compiler (tm) v1.006-14-prod Required arguments: input Table as CSV files and/or URLs or Command above Optional arguments: -h show this help message and exit -version, --version show program's version number and exit Basic options: -headerless Headerless CSV input file. -target TARGET Specify target column by name or number. Default: last column of table. -ignorecolumns IGNORECOLUMNS Comma-separated list of columns to ignore by name or number. -rank [ATTRIBUTERANK] Select the optimal subset of columns for accuracy on held out data If optional parameter N is given, select the optimal N columns. Works best for DT. -measureonly Only output measurements, no predictor is built. -f FORCEMODEL Force model type: DT, NN, RF Default: RF -nosplit Use all of the data for training. Default: dataset is split between training and validation. -split FORCESPLIT Pass it an integer between 50 and 90 telling forcing our system to use that percent of the data for training, and the rest for validation Intermediate options: -nsamples NSAMPLES Train only on a subset of N random samples of the dataset. Default: entire dataset. -ignoreclasses IGNORELABELS Comma-separated list of classes to ignore. -usecolumns IMPORTANTCOLUMNS Comma-separated list of columns by name or number used to build the predictor. -o OUTPUT Predictor filename. Default: a.py -v Verbose output -q Quiet operation. -y Answers yes to all overwrite questions. Advanced options: -e EFFORT Increase compute time to improve accuracy. 1=<EFFORT<100. Default: 1 -biasmeter Measure model bias -novalidation Do not measure validation scores for created predictor. -balance Treat classes as if they were balanced (only active for NN). -O OPTIMIZE Maximize true positives towards a single class. -nofun Stop compilation if there are warnings. -modelonly Perform only the measurements needed to build the model. Examples: Measure and build a random forest predictor for titanic brainome https://download.brainome.ai/data/public/titanic_train.csv Build a better predictor by ignoring columns: brainome titanic_train.csv -ignorecolumns "PassengerId,Name" -target Survived Automatically select the important columns by using ranking: brainome titanic_train.csv -rank -target Survived Build a neural network model with effort of 5: brainome titanic_train.csv -f NN -e 5 -target Survived Measure headerless dataset: brainome https://download.brainome.ai/data/public/bank.csv -headerless -measureonly Full documentation can be found at https://www.brainome.ai/documentation
Additional documentation can be found at