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| author | Yanbo Liang <ybliang8@gmail.com> | 2015-11-05 08:59:06 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-11-05 08:59:06 -0800 |
| commit | 72634f27e3110fd7f5bfca498752f69d0b1f873c (patch) | |
| tree | 4791c9263c5ce30db6f8b3063443f0d66dcf8546 /docs/ml-linear-methods.md | |
| parent | 77488fb8e586103ba4c0858b73e1715f1a66671f (diff) | |
| download | spark-72634f27e3110fd7f5bfca498752f69d0b1f873c.tar.gz spark-72634f27e3110fd7f5bfca498752f69d0b1f873c.tar.bz2 spark-72634f27e3110fd7f5bfca498752f69d0b1f873c.zip | |
[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 <ybliang8@gmail.com>
Closes #9493 from yanboliang/docs-coefficients.
Diffstat (limited to 'docs/ml-linear-methods.md')
| -rw-r--r-- | docs/ml-linear-methods.md | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md index 4e94e2f9c7..16e2ee7129 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. |