Logistic-regression subcommand

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

bigmler logistic-regression --train data/iris.csv

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

bigmler logistic-regression --dataset dataset/53b1f71437203f5ac30004ed \
                --logistic-fields="-sepal length"

that would exclude the field sepal length from the logistic regression model creation input fields. You can also change some parameters in the logistic regression model, like the bias (scale of the intercept term), c (the strength of the regularization map) or eps (stopping criteria for solver).

bigmler logistic-regression --dataset dataset/53b1f71437203f5ac30004ed \
                            --bias --c 5 --eps 0.5

with this code, the logistic regression is built using an independent term, the step in the regularization is 5 and the difference between the results from the current and last iterations is 0.5.

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

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

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

The logistic regression can be used to assign a prediction to each new input data set. The command

bigmler logistic-regression \
        --logistic-regression logisticregression/53b1f71435203f5ac30005c0 \
        --test data/test_iris.csv

would produce a file predictions.csv with the predictions associated to each input. When the command is executed, the logistic regression information is downloaded to your local computer and the logistic 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 logistic-regression \
        --logistic-regression logisticregression/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}

Logistic regression Subcommand Options

--logistic-regression LOGISTIC_R	BigML logistic regression Id
--logistic-regressions PATH	Path to a file containing logisticregression/ids. One logistic regression per line (e.g., logisticregression/4f824203ce80051)
--no-logistic-regression	No logistic regression will be generated
--logistic-fields LOGISTIC_FIELDS	Comma-separated list of fields that will be used in the logistic regression construction
--normalize	Normalize feature vectors in training and prediction inputs
--no-missing-numerics	Avoids the default behaviour, which creates a new coefficient for missings in numeric fields. Missing rows are discarded.
--no-bias	Avoids default behaviour. The logistic regression will have no intercept term.
--no-balance-fields	Avoids default behaviour. No automatic field balance.
--field-codings FIELD_CODINGS	Numeric encoding for categorical fields (default one-hot encoding)
--c C	Strength of the regularization step
--eps EPS	Stopping criteria for solver.
--logistic-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 logistic regression creation call
--logistic-regression-file PATH	Path to a JSON file containing the logistic regression info