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Diffstat (limited to 'docs/mllib-optimization.md')
| -rw-r--r-- | docs/mllib-optimization.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index ad7bcd9bfd..f90b66f8e2 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -1,7 +1,7 @@ --- layout: global -title: Optimization - MLlib -displayTitle: <a href="mllib-guide.html">MLlib</a> - Optimization +title: Optimization - spark.mllib +displayTitle: Optimization - spark.mllib --- * Table of contents @@ -87,7 +87,7 @@ in the `$t$`-th iteration, with the input parameter `$s=$ stepSize`. Note that s step-size for SGD methods can often be delicate in practice and is a topic of active research. **Gradients.** -A table of (sub)gradients of the machine learning methods implemented in MLlib, is available in +A table of (sub)gradients of the machine learning methods implemented in `spark.mllib`, is available in the <a href="mllib-classification-regression.html">classification and regression</a> section. @@ -140,7 +140,7 @@ other first-order optimization. ### Choosing an Optimization Method -[Linear methods](mllib-linear-methods.html) use optimization internally, and some linear methods in MLlib support both SGD and L-BFGS. +[Linear methods](mllib-linear-methods.html) use optimization internally, and some linear methods in `spark.mllib` support both SGD and L-BFGS. Different optimization methods can have different convergence guarantees depending on the properties of the objective function, and we cannot cover the literature here. In general, when L-BFGS is available, we recommend using it instead of SGD since L-BFGS tends to converge faster (in fewer iterations). |