| <!DOCTYPE html><html><head><title>R: Logistic Regression Model</title> |
| <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" /> |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.css"> |
| <script type="text/javascript"> |
| const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"}; |
| function processMathHTML() { |
| var l = document.getElementsByClassName('reqn'); |
| for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } |
| return; |
| }</script> |
| <script defer src="https://cdn.jsdelivr.net/npm/katex@0.15.3/dist/katex.min.js" |
| onload="processMathHTML();"></script> |
| <link rel="stylesheet" type="text/css" href="R.css" /> |
| |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> |
| <script>hljs.initHighlightingOnLoad();</script> |
| </head><body><div class="container"> |
| |
| <table style="width: 100%;"><tr><td>spark.logit {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> |
| |
| <h2>Logistic Regression Model</h2> |
| |
| <h3>Description</h3> |
| |
| <p>Fits an logistic regression model against a SparkDataFrame. It supports "binomial": Binary |
| logistic regression with pivoting; "multinomial": Multinomial logistic (softmax) regression |
| without pivoting, similar to glmnet. Users can print, make predictions on the produced model |
| and save the model to the input path. |
| </p> |
| |
| |
| <h3>Usage</h3> |
| |
| <pre><code class='language-R'>spark.logit(data, formula, ...) |
| |
| ## S4 method for signature 'SparkDataFrame,formula' |
| spark.logit( |
| data, |
| formula, |
| regParam = 0, |
| elasticNetParam = 0, |
| maxIter = 100, |
| tol = 1e-06, |
| family = "auto", |
| standardization = TRUE, |
| thresholds = 0.5, |
| weightCol = NULL, |
| aggregationDepth = 2, |
| lowerBoundsOnCoefficients = NULL, |
| upperBoundsOnCoefficients = NULL, |
| lowerBoundsOnIntercepts = NULL, |
| upperBoundsOnIntercepts = NULL, |
| handleInvalid = c("error", "keep", "skip") |
| ) |
| |
| ## S4 method for signature 'LogisticRegressionModel' |
| summary(object) |
| |
| ## S4 method for signature 'LogisticRegressionModel' |
| predict(object, newData) |
| |
| ## S4 method for signature 'LogisticRegressionModel,character' |
| write.ml(object, path, overwrite = FALSE) |
| </code></pre> |
| |
| |
| <h3>Arguments</h3> |
| |
| <table> |
| <tr style="vertical-align: top;"><td><code>data</code></td> |
| <td> |
| <p>SparkDataFrame for training.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>formula</code></td> |
| <td> |
| <p>A symbolic description of the model to be fitted. Currently only a few formula |
| operators are supported, including '~', '.', ':', '+', and '-'.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>...</code></td> |
| <td> |
| <p>additional arguments passed to the method.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>regParam</code></td> |
| <td> |
| <p>the regularization parameter.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>elasticNetParam</code></td> |
| <td> |
| <p>the ElasticNet mixing parameter. For alpha = 0.0, the penalty is an L2 |
| penalty. For alpha = 1.0, it is an L1 penalty. For 0.0 < alpha < 1.0, |
| the penalty is a combination of L1 and L2. Default is 0.0 which is an |
| L2 penalty.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>maxIter</code></td> |
| <td> |
| <p>maximum iteration number.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>tol</code></td> |
| <td> |
| <p>convergence tolerance of iterations.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>family</code></td> |
| <td> |
| <p>the name of family which is a description of the label distribution to be used |
| in the model. |
| Supported options: |
| </p> |
| |
| <ul> |
| <li><p>"auto": Automatically select the family based on the number of classes: |
| If number of classes == 1 || number of classes == 2, set to "binomial". |
| Else, set to "multinomial". |
| </p> |
| </li> |
| <li><p>"binomial": Binary logistic regression with pivoting. |
| </p> |
| </li> |
| <li><p>"multinomial": Multinomial logistic (softmax) regression without |
| pivoting. |
| </p> |
| </li></ul> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>standardization</code></td> |
| <td> |
| <p>whether to standardize the training features before fitting the model. |
| The coefficients of models will be always returned on the original scale, |
| so it will be transparent for users. Note that with/without |
| standardization, the models should be always converged to the same |
| solution when no regularization is applied. Default is TRUE, same as |
| glmnet.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>thresholds</code></td> |
| <td> |
| <p>in binary classification, in range [0, 1]. If the estimated probability of |
| class label 1 is > threshold, then predict 1, else 0. A high threshold |
| encourages the model to predict 0 more often; a low threshold encourages the |
| model to predict 1 more often. Note: Setting this with threshold p is |
| equivalent to setting thresholds c(1-p, p). In multiclass (or binary) |
| classification to adjust the probability of predicting each class. Array must |
| have length equal to the number of classes, with values > 0, excepting that |
| at most one value may be 0. The class with largest value p/t is predicted, |
| where p is the original probability of that class and t is the class's |
| threshold.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>weightCol</code></td> |
| <td> |
| <p>The weight column name.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>aggregationDepth</code></td> |
| <td> |
| <p>The depth for treeAggregate (greater than or equal to 2). If the |
| dimensions of features or the number of partitions are large, this param |
| could be adjusted to a larger size. This is an expert parameter. Default |
| value should be good for most cases.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>lowerBoundsOnCoefficients</code></td> |
| <td> |
| <p>The lower bounds on coefficients if fitting under bound |
| constrained optimization. |
| The bound matrix must be compatible with the shape (1, number |
| of features) for binomial regression, or (number of classes, |
| number of features) for multinomial regression. |
| It is a R matrix.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>upperBoundsOnCoefficients</code></td> |
| <td> |
| <p>The upper bounds on coefficients if fitting under bound |
| constrained optimization. |
| The bound matrix must be compatible with the shape (1, number |
| of features) for binomial regression, or (number of classes, |
| number of features) for multinomial regression. |
| It is a R matrix.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>lowerBoundsOnIntercepts</code></td> |
| <td> |
| <p>The lower bounds on intercepts if fitting under bound constrained |
| optimization. |
| The bounds vector size must be equal to 1 for binomial regression, |
| or the number |
| of classes for multinomial regression.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>upperBoundsOnIntercepts</code></td> |
| <td> |
| <p>The upper bounds on intercepts if fitting under bound constrained |
| optimization. |
| The bound vector size must be equal to 1 for binomial regression, |
| or the number of classes for multinomial regression.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>handleInvalid</code></td> |
| <td> |
| <p>How to handle invalid data (unseen labels or NULL values) in features and |
| label column of string type. |
| Supported options: "skip" (filter out rows with invalid data), |
| "error" (throw an error), "keep" (put invalid data in |
| a special additional bucket, at index numLabels). Default |
| is "error".</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>object</code></td> |
| <td> |
| <p>an LogisticRegressionModel fitted by <code>spark.logit</code>.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>newData</code></td> |
| <td> |
| <p>a SparkDataFrame for testing.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>path</code></td> |
| <td> |
| <p>The directory where the model is saved.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>overwrite</code></td> |
| <td> |
| <p>Overwrites or not if the output path already exists. Default is FALSE |
| which means throw exception if the output path exists.</p> |
| </td></tr> |
| </table> |
| |
| |
| <h3>Value</h3> |
| |
| <p><code>spark.logit</code> returns a fitted logistic regression model. |
| </p> |
| <p><code>summary</code> returns summary information of the fitted model, which is a list. |
| The list includes <code>coefficients</code> (coefficients matrix of the fitted model). |
| </p> |
| <p><code>predict</code> returns the predicted values based on an LogisticRegressionModel. |
| </p> |
| |
| |
| <h3>Note</h3> |
| |
| <p>spark.logit since 2.1.0 |
| </p> |
| <p>summary(LogisticRegressionModel) since 2.1.0 |
| </p> |
| <p>predict(LogisticRegressionModel) since 2.1.0 |
| </p> |
| <p>write.ml(LogisticRegression, character) since 2.1.0 |
| </p> |
| |
| |
| <h3>Examples</h3> |
| |
| <pre><code class="r">## Not run: |
| ##D sparkR.session() |
| ##D # binary logistic regression |
| ##D t <- as.data.frame(Titanic) |
| ##D training <- createDataFrame(t) |
| ##D model <- spark.logit(training, Survived ~ ., regParam = 0.5) |
| ##D summary <- summary(model) |
| ##D |
| ##D # fitted values on training data |
| ##D fitted <- predict(model, training) |
| ##D |
| ##D # save fitted model to input path |
| ##D path <- "path/to/model" |
| ##D write.ml(model, path) |
| ##D |
| ##D # can also read back the saved model and predict |
| ##D # Note that summary deos not work on loaded model |
| ##D savedModel <- read.ml(path) |
| ##D summary(savedModel) |
| ##D |
| ##D # binary logistic regression against two classes with |
| ##D # upperBoundsOnCoefficients and upperBoundsOnIntercepts |
| ##D ubc <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4) |
| ##D model <- spark.logit(training, Species ~ ., |
| ##D upperBoundsOnCoefficients = ubc, |
| ##D upperBoundsOnIntercepts = 1.0) |
| ##D |
| ##D # multinomial logistic regression |
| ##D model <- spark.logit(training, Class ~ ., regParam = 0.5) |
| ##D summary <- summary(model) |
| ##D |
| ##D # multinomial logistic regression with |
| ##D # lowerBoundsOnCoefficients and lowerBoundsOnIntercepts |
| ##D lbc <- matrix(c(0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0), nrow = 2, ncol = 4) |
| ##D lbi <- as.array(c(0.0, 0.0)) |
| ##D model <- spark.logit(training, Species ~ ., family = "multinomial", |
| ##D lowerBoundsOnCoefficients = lbc, |
| ##D lowerBoundsOnIntercepts = lbi) |
| ## End(Not run) |
| </code></pre> |
| |
| |
| <hr /><div style="text-align: center;">[Package <em>SparkR</em> version 3.2.2 <a href="00Index.html">Index</a>]</div> |
| </div> |
| </body></html> |
