Linear-regression subcommand

The bigmler linear-regression subcommand generates all the resources needed to buid a linear regression model and use it to predict. The linear regression model is a supervised learning method for solving regression problems. It predicts the objective field class as a linear function whose argument are the rest of features. The simplest call to build a linear regression is

bigmler linear-regression --train data/grades.csv

uploads the data in the data/grades.csv file and generates the corresponding source, dataset and linear regression objects in BigML. You can use any of the generated objects to produce new linear regressions. For instance, you could set a subgroup of the fields of the generated dataset to produce a different linear regression model by using

bigmler linear-regression --dataset dataset/53b1f71437203f5ac30004ed \
                          --linear-fields="-Prefix"

that would exclude the field Prefix from the linear regression model creation input fields. You can also change some parameters in the linear regression model, like the bias (intercept term)).

bigmler linear-regression --dataset dataset/53b1f71437203f5ac30004ed \
                          --no-bias

with this code, the linear regression is built without using an independent term.

Similarly to models and datasets, the generated linear regressions can be shared using the --shared option, e.g.

bigmler linear-regression --source source/53b1f71437203f5ac30004e0 \
                          --shared

will generate a secret link for both the created dataset and linear regressions, that can be used to share the resource selectively.

Linear regressions can produce a prediction for each new input data set. The command

bigmler linear-regression \
        --linear-regression linearregression/53b1f71435203f5ac30005c0 \
        --test data/test_grades.csv

would produce a file predictions.csv with the predictions associated to each input. When the command is executed, the linear regression information is downloaded to your local computer and the linear regression predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the predictions remotely, you can do so too

bigmler linear-regression
        --linear-regression linearregression/53b1f71435203f5ac30005c0 \
        --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch prediction also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output. Other output configurations can be set by using the --batch-prediction-attributes option pointing to a JSON file that contains the desired attributes, like:

{"probabilities": true,
 "all_fields": true}

Linear regression Subcommand Options

--linear-regression LINEAR_R	BigML linear regression Id
--linear-regressions PATH	Path to a file containing linearregression/ids. One linear regression per line (e.g., linearregression/4f824203ce80051)
--no-linear-regression	No linear regression will be generated
--linear-fields LINEAR_FIELDS	Comma-separated list of fields that will be used in the linear regression construction
--no-bias	Avoids default behaviour. The linear regression will have no intercept term.
--field-codings FIELD_CODINGS	Numeric encoding for categorical fields (default one-hot encoding)
--linear-regression-attributes PATH	Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the linear regression creation call
--linear-regression-file PATH	Path to a JSON file containing the linear regression info