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How does Spark MLlib work with linear regression?
Linear regression. The interface for working with linear regression models and model summaries is similar to the logistic regression case. When fitting LinearRegressionModel without intercept on dataset with constant nonzero column by “l-bfgs” solver, Spark MLlib outputs zero coefficients for constant nonzero columns.
How is multinomial logistic regression used in spark?
Multinomial logistic regression can be used for binary classification by setting the family param to “multinomial”. It will produce two sets of coefficients and two intercepts. When fitting LogisticRegressionModel without intercept on dataset with constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero columns.
How are random forests used in spark.ml?
Random forests are a popular family of classification and regression methods. More information about the spark.ml implementation can be found further in the section on random forests. The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
How many coefficients are there in spark glmnet?
It will produce two sets of coefficients and two intercepts. When fitting LogisticRegressionModel without intercept on dataset with constant nonzero column, Spark MLlib outputs zero coefficients for constant nonzero columns. This behavior is the same as R glmnet but different from LIBSVM.
What kind of regression is supported in spark?
Spark’s GeneralizedLinearRegression interface allows for flexible specification of GLMs which can be used for various types of prediction problems including linear regression, Poisson regression, logistic regression, and others. Currently in spark.ml, only a subset of the exponential family distributions are supported and they are listed below.
How to train binomial logistic regression in spark?
The following example shows how to train binomial and multinomial logistic regression models for binary classification with elastic net regularization. elasticNetParam corresponds to α and regParam corresponds to λ. More details on parameters can be found in the Scala API documentation.
What are the metrics for machine learning in spark?
Specific machine learning algorithms fall under broader types of machine learning applications like classification, regression, clustering, etc. Each of these types have well-established metrics for performance evaluation and those metrics that are currently available in spark.mllib are detailed in this section.