Repository: spark Updated Branches: refs/heads/master 77488fb8e -> 72634f27e
[MINOR][ML][DOC] Rename weights to coefficients in user guide We should use ```coefficients``` rather than ```weights``` in user guide that freshman can get the right conventional name at the outset. mengxr vectorijk Author: Yanbo Liang <yblia...@gmail.com> Closes #9493 from yanboliang/docs-coefficients. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/72634f27 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/72634f27 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/72634f27 Branch: refs/heads/master Commit: 72634f27e3110fd7f5bfca498752f69d0b1f873c Parents: 77488fb Author: Yanbo Liang <yblia...@gmail.com> Authored: Thu Nov 5 08:59:06 2015 -0800 Committer: Xiangrui Meng <m...@databricks.com> Committed: Thu Nov 5 08:59:06 2015 -0800 ---------------------------------------------------------------------- docs/ml-linear-methods.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/72634f27/docs/ml-linear-methods.md ---------------------------------------------------------------------- diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md index 4e94e2f..16e2ee7 100644 --- a/docs/ml-linear-methods.md +++ b/docs/ml-linear-methods.md @@ -71,8 +71,8 @@ val lr = new LogisticRegression() // Fit the model val lrModel = lr.fit(training) -// Print the weights and intercept for logistic regression -println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}") +// Print the coefficients and intercept for logistic regression +println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") {% endhighlight %} </div> @@ -105,8 +105,8 @@ public class LogisticRegressionWithElasticNetExample { // Fit the model LogisticRegressionModel lrModel = lr.fit(training); - // Print the weights and intercept for logistic regression - System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept()); + // Print the coefficients and intercept for logistic regression + System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept()); } } {% endhighlight %} @@ -124,8 +124,8 @@ lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) # Fit the model lrModel = lr.fit(training) -# Print the weights and intercept for logistic regression -print("Weights: " + str(lrModel.weights)) +# Print the coefficients and intercept for logistic regression +print("Coefficients: " + str(lrModel.coefficients)) print("Intercept: " + str(lrModel.intercept)) {% endhighlight %} </div> @@ -258,8 +258,8 @@ val lr = new LinearRegression() // Fit the model val lrModel = lr.fit(training) -// Print the weights and intercept for linear regression -println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}") +// Print the coefficients and intercept for linear regression +println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") // Summarize the model over the training set and print out some metrics val trainingSummary = lrModel.summary @@ -302,8 +302,8 @@ public class LinearRegressionWithElasticNetExample { // Fit the model LinearRegressionModel lrModel = lr.fit(training); - // Print the weights and intercept for linear regression - System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept()); + // Print the coefficients and intercept for linear regression + System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept()); // Summarize the model over the training set and print out some metrics LinearRegressionTrainingSummary trainingSummary = lrModel.summary(); @@ -330,8 +330,8 @@ lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) # Fit the model lrModel = lr.fit(training) -# Print the weights and intercept for linear regression -print("Weights: " + str(lrModel.weights)) +# Print the coefficients and intercept for linear regression +print("Coefficients: " + str(lrModel.coefficients)) print("Intercept: " + str(lrModel.intercept)) # Linear regression model summary is not yet supported in Python. --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
